Multiple sensing electrodes for regenerative peripheral nerve interface (RPNI)

Abstract

Systems and methods include an implant device having processing circuitry configured to receive electrical signals from a free tissue graft via a plurality of electrodes attached to the free tissue graft, the free tissue graft operatively attached to a nerve of a subject. The processing circuitry is configured to generate processed signal data corresponding to the received electrical signals from each of the electrodes, and to transmit the processed signal data to the prosthesis controller. The prosthetic controller is configured to control the prosthetic device based on the processed signal data transmitted from the processing circuitry to control the prosthetic device to perform a plurality of different functions based on the processed signal data, the processed signal data generated based on the received electrical signals from each of the plurality of electrodes.

Description

[0001] Government License Rights This invention was made with government support under N66001-16-1-4006 awarded by the Office of Naval Research, NS105132 and NS104584 awarded by the National Institutes of Health, and W81XWH-21-1-0429 awarded by the U.S. Army Medical Research and Materiel Command. The government has certain rights in the invention. Cross Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 467,499, filed May 18, 2023. The entire disclosure of the above application is incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates to regenerative peripheral nerve interface (RPNI) devices. BACKGROUND

[0004] This section provides background information related to the present disclosure which is not necessarily prior art.

[0005] There is a need to receive and record signals from a nerve (e.g., a human nerve) for subsequent processing and, for example, for controlling a prosthetic limb. A free tissue graft can be attached to a portion of a nerve (e.g., a fascicle) and electrical signals from the nerve can be amplified by the free tissue graft. However, there is a need for systems and methods that effectively and efficiently process the amplified signals from the nerve and that effectively and efficiently control a prosthesis based on the processed signals. Additionally, there is a need for systems and methods that effectively and efficiently transmit sensory feedback signals from the prosthesis through the free tissue graft to the nerve. SUMMARY

[0006] This section provides a summary of the present disclosure and is not a comprehensive disclosure of the full scope of the present disclosure or all features of the present disclosure.

[0007] A system includes an implant device having processing circuitry configured to receive electrical signals from a free tissue graft surgically attached to a nerve of a subject; wherein the electrical signals are received by a plurality of electrodes attached to and in electrical communication with the free tissue graft; the free tissue graft is surgically attached to the subject such that the free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the free tissue graft is an autograft of tissue de-vascularized and de-innervated prior to being surgically attached to the subject from the subject; the processing circuitry is further configured to process the received electrical signals from the plurality of electrodes, generate processed signal data corresponding to the received electrical signals from each of the plurality of electrodes, and transmit the processed signal data to a prosthesis controller. The nerve re-innervates the plurality of free tissue grafts after the plurality of free tissue grafts are surgically attached to the nerve. The prosthesis controller is configured to control a prosthesis device based on the processed signal data transmitted from the processing circuitry of the implant device. The prosthesis controller controls the prosthesis device to perform a plurality of different functions based on the processed signal data generated based on the received electrical signals from each of the plurality of electrodes.

[0008] A method includes a processing circuitry of an implant device receiving electrical signals from a free tissue graft surgically attached to a nerve of a subject; wherein the electrical signals are received by a plurality of electrodes attached to and in electrical communication with the free tissue graft; the free tissue graft is surgically attached to the subject such that the free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the free tissue graft is an autograft of tissue de-vascularized and de-innervated prior to being surgically attached to the subject from the subject. The method further includes processing, with the processing circuitry, the received electrical signals from the plurality of electrodes. The method further includes generating, with the processing circuitry, processed signal data corresponding to the received electrical signals from each of the plurality of electrodes. The method further includes transmitting, with the processing circuitry, the processed signal data to a prosthesis controller. The nerve re-innervates the plurality of free tissue grafts after the plurality of free tissue grafts are surgically attached to the nerve. The prosthesis controller is configured to control a prosthesis device based on the processed signal data transmitted from the processing circuitry of the implant device. The prosthesis controller controls the prosthesis device to perform a plurality of different functions based on the processed signal data generated based on the received electrical signals from each of the plurality of electrodes.

[0009] Further areas of applicability will become apparent from the description provided herein. The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0010] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure.

[0011] Figure 1 A regenerative peripheral nerve interface is shown in accordance with certain aspects of the present disclosure.

[0012] Figure 2 Three free muscle grafts after acquisition from a subject, and photographs showing a surgical procedure for attaching the free muscle grafts to nerve bundles of the subject by surgery, are shown in accordance with certain aspects of the present disclosure.

[0013] Figure 3 Another regenerative peripheral nerve interface and prosthetic device is shown in accordance with certain aspects of the present disclosure.

[0014] Figure 4 Another regenerative peripheral nerve interface is shown in accordance with certain aspects of the present disclosure.

[0015] Figure 5 is a block diagram of an implant device in accordance with certain aspects of the present disclosure.

[0016] Figure 6 is a photograph of a hardware device chip in accordance with certain aspects of the present disclosure.

[0017] Figure 7 is a block diagram of processing circuitry with memory and communication circuitry in accordance with certain aspects of the present disclosure.

[0018] Figure 8 is a flowchart depicting an example control algorithm for recording amplified neural signal data in accordance with certain aspects of the present disclosure.

[0019] Figure 9 is a flowchart depicting an example control algorithm for controlling a prosthetic limb in accordance with certain aspects of the present disclosure.

[0020] Figure 10 is a flowchart depicting an example control algorithm for decoding signals for controlling a prosthetic limb in accordance with certain aspects of the present disclosure.

[0021] Figure 11 is a plot showing neural signal data corresponding to the onset of a flexion movement.

[0022] Figure 12is a flowchart depicting an example control algorithm for monitoring a neuropathic pain signal of a nerve in accordance with certain aspects of the present disclosure.

[0023] Figure 13 is a flowchart depicting an example control algorithm for monitoring a neuropathic bladder contraction signal in accordance with certain aspects of the present disclosure.

[0024] Figure 14 is a flowchart depicting an example control algorithm for stimulating a nerve based on a sensed pressure signal from a prosthetic device in accordance with certain aspects of the present disclosure.

[0025] Figure 15 is a plot showing nerve signal data corresponding to the onset of a finger flexion movement.

[0026] Figure 16 is a set of plots showing nerve signal data from an implanted regenerative peripheral nerve interface.

[0027] Figure 17 is a set of plots showing nerve signal data from an implanted regenerative peripheral nerve interface and from a controlling muscle.

[0028] Figure 18 is a plot showing nerve signal data from an implanted regenerative peripheral nerve interface.

[0029] Figure 19 is a plot of predicted finger flexion percentage and actual finger flexion percentage over time.

[0030] Figure 20 A system for controlling a prosthesis based on an amplified nerve signal in accordance with the present disclosure is shown.

[0031] Figure 21 A system for controlling a prosthesis based on an amplified nerve signal in accordance with the present disclosure is further shown.

[0032] Figure 22 An electrical lead for a system for controlling a prosthesis based on an amplified nerve signal in accordance with the present disclosure is shown.

[0033] Figure 23 An electrical lead for a system for controlling a prosthesis based on an amplified nerve signal in accordance with the present disclosure is further shown.

[0034] Figure 24A An implant device including a head for receiving an eight-contact connector in accordance with the present disclosure is shown.

[0035] Figure 24B Another embodiment of an electrical lead for a system for controlling a prosthesis based on an amplified nerve signal in accordance with the present disclosure is shown.

[0036] Figure 25 A protection circuit for an implant device according to the present disclosure is shown.

[0037] Figure 26 A functional block diagram for processing an amplified neural signal according to the present disclosure is shown.

[0038] Figure 27 A prosthetic controller and prosthetic hand according to the present disclosure are shown.

[0039] Figure 28 An external programmer charger and inductive charging pad according to the present disclosure are shown.

[0040] Figure 29 An external programmer device in communication with an implant device and a prosthetic controller according to the present disclosure is shown.

[0041] Figure 30 Another embodiment of a system for controlling a prosthetic based on an amplified neural signal according to the present disclosure is shown.

[0042] Figure 31 An algorithm for generating a prosthetic motion command and performing stimulation in alternating time periods is shown.

[0043] Figure 32 Another algorithm for generating a prosthetic motion command and performing stimulation in alternating time periods, while generating an estimate of the prosthetic motion command during the time period in which stimulation is performed, is shown.

[0044] Figure 33 An algorithm for generating a prosthetic motion command and performing stimulation, while performing artifact estimation and subtraction to remove artifacts caused by stimulation from the sensed signal, is shown.

[0045] Figure 34 Another regenerative peripheral nerve interface according to certain aspects of the present disclosure is shown.

[0046] Figure 35 Histological imaging showing different motor units forming neuromuscular junctions within a single RPNI according to the present disclosure is shown.

[0047] Figure 36 Different regions of RPNI on the median nerve that respond to different individual finger motions and thumb motions when viewed under ultrasound according to the present disclosure are shown.

[0048] Figure 37 Different regions of RPNI on the ulnar nerve that respond to different individual finger motions and thumb motions when viewed under ultrasound according to the present disclosure are shown.

[0049] Figure 38A A graph showing signals for small finger flexion and finger adduction according to the present disclosure is shown.

[0050] Figure 38B A block diagram showing signal processing of RPNI signals to generate motion commands according to the present disclosure is shown.

[0051] Figure 39 A block diagram showing processing circuitry for signal processing of RPNI signals to generate motion commands according to the present disclosure is shown.

[0052] Figure 40 A plurality of sensing electrodes of an RPNI according to the present disclosure is shown.

[0053] Figure 41 Processing circuitry receiving input from a plurality of input channels of an RPNI according to the present disclosure is shown.

[0054] Figure 42 A plurality of networking modules of an implant device receiving input from a plurality of input channels of an RPNI according to the present disclosure is shown.

[0055] Figure 43A And Figure 43B A photograph of an RPNI with a plurality of sensing electrodes according to the present disclosure is shown.

[0056] Figure 44 A graph showing signals sensed from a plurality of sensing electrodes of an RPNI according to the present disclosure is shown.

[0057] Figure 45 A photograph of an RPNI with a plurality of sensing electrodes according to the present disclosure is shown.

[0058] Figure 46 A graph showing signals sensed from a plurality of sensing electrodes of an RPNI according to the present disclosure is shown.

[0059] Figure 47 A composite regenerative peripheral nerve interface (C-RPNI) according to certain aspects of the present disclosure is shown.

[0060] Figure 48 A block diagram of an implant device with processing circuitry and a plurality of C-RPNIs according to the present disclosure is shown.

[0061] Figure 49 A graph of signals collected by alternating stimulation and recording on the same nerve according to the present disclosure is shown.

[0062] Figure 50 A graph showing stable perceptual detection thresholds of a patient's RPNI over time according to the present disclosure is shown.

[0063] Figure 51 A hand map depicting body position accuracy of involved sensation according to the present disclosure is shown.

[0064] Figure 52 Another composite regenerative peripheral nerve interface (C-RPNI) according to certain aspects of the present disclosure is shown.

[0065] Figure 53 A block diagram of processing circuitry and multiple C-RPNIs according to the present disclosure is shown.

[0066] Figure 54 Another block diagram of processing circuitry and multiple C-RPNIs according to the present disclosure is shown.

[0067] Figure 55 A block diagram of sensing and stimulation implant with high channel count according to the present disclosure is shown.

[0068] Figure 56 Multiple networking modules of an implant device and multiple C-RPNIs according to the present disclosure are shown.

[0069] Figure 57 Multiple networking modules of an implant device and multiple C-RPNIs according to the present disclosure are shown.

[0070] Figure 58 A photograph of a C-RPNI construct.

[0071] Figure 59 A photograph of an electrical diagnostic setup for generation and recording according to the present disclosure by stimulating a dermal graft and recording afferent activity with a nerve cuff.

[0072] Figure 60 A graph of data collected by alternating stimulation and recording on the same nerve according to the present disclosure is shown.

[0073] Figure 61 A composite cuff regenerative peripheral nerve interface (CC-RPNI) according to certain aspects of the present disclosure is shown.

[0074] Figure 62 A photograph of a CC-RPNI construct according to the present disclosure is shown.

[0075] Figure 63 A photograph of a setup for generating and recording signals with a CC-RPNI according to the present disclosure is shown.

[0076] Figure 64 is a graph showing incoming signaling and outgoing signaling with a CC-RPNI construct according to the present disclosure.

[0077] Figure 65 shows electrophysiology experiment setup on a C-RPNI construct according to the present disclosure.

[0078] Figure 66 shows a graph showing incoming nerve action potentials and electrical stimulation according to the present disclosure.

[0079] Figure 67 shows a construct with a single tissue circumferentially surrounding a nerve, physically separated from a different tissue attached to a terminal of the nerve, according to the present disclosure.

[0080] Figure 68 is a photograph of a construct with a single tissue circumferentially surrounding a nerve, physically separated from a different tissue attached to a terminal of the nerve, according to the present disclosure.

[0081] Figure 69 shows a graph showing incoming nerve action of a construct with a single tissue circumferentially surrounding a nerve, physically separated from a different tissue attached to a terminal of the nerve, according to the present disclosure.

[0082] Figure 70 shows a graph showing incoming signaling and outgoing signaling of a construct with a single tissue circumferentially surrounding a nerve, physically separated from a different tissue attached to a terminal of the nerve, according to the present disclosure.

[0083] Corresponding reference numerals in the several figures of the drawings refer to components corresponding to one another.

[0084] It should be noted that the drawings set forth herein are intended to exemplify the general architecture of the methods, apparatus and materials that can be employed in accordance with the disclosure, and that the drawings are not intended to limit the scope of particular embodiments within the present disclosure in any way. DETAILED DESCRIPTION

[0085] Example embodiments will now be described with reference to the drawings. A non-limiting discussion of terms and phrases intended to aid in the understanding of the present disclosure is provided at the end of this DETAILED DESCRIPTION.

[0086] In various aspects, the present disclosure provides methods for amplifying signals from a portion of a nerve (e.g., individual fascicles) to levels higher than those generated by any conventional methods or techniques, and receiving the signals. Specifically, as described in greater detail below, the present disclosure provides methods for amplifying signals from a portion of a nerve, such as individual fascicles, to peak-to-peak levels of greater than or equal to about 150 microvolts peak-to-peak (µV pp), in some examples, greater than or equal to about 250 µV pp or 500 µV pp, and, for example, up to about 1000 µV pp or more, and receiving these signals. As mentioned above, signals detected by previous neural interface systems are typically less than 100 µV pp when recorded from within a nerve, and less than 10 µV pp when recorded from a cuff around a nerve. In certain aspects, the present disclosure provides implantable neural interface devices, which can also be interchangeably referred to as regenerative peripheral nerve interface (RPNI) devices, that facilitate amplification of signals from individual fascicles to greater than or equal to about 150 µV pp, and, in some examples, to greater than or equal to about 250 µV pp or 500 µV pp, and, for example, up to about 1000 µV pp or more.

[0087] Reference Figure 1FIG. 1 shows a neural interface system 4 of a subject or patient. The subject can be an animal with a complex nervous system, such as a mammal (e.g., a human, a primate, or a companion animal). A portion of the subject's nerve 6 (e.g., nerve terminals) can be damaged or severed, e.g., the nerve terminals are completely or partially damaged due to injury, disease, or surgery. In certain aspects, the method can include surgically dividing, severing, cutting, and / or transecting a portion of the nerve 6 into one or more separate branches or fascicles 8. It should be noted that in certain variations, the method can include isolating a portion of interest of the nerve 6 to produce the one or more separate branches or fascicles 8. The one or more separate branches or fascicles 8 are each placed within a free tissue graft 10. In certain aspects, the free tissue graft 10 can be an autograft of muscle tissue or dermal tissue previously harvested from the subject. In certain preferred aspects, the free tissue graft 10 is muscle tissue. The free tissue graft 10 is harvested or excised such that it has a standard predetermined volume or size that depends on the size of the branches or fascicles 8. When the free tissue graft 10 is harvested, the tissue graft is devascularized and the native blood vessels are no longer functional. As will be described in greater detail below, the predetermined volume of the free tissue graft 10 can be selected to be small enough such that it is appropriately revascularized by collateral blood flow, allowing the free tissue graft 10 to thrive, while providing a sufficient size area or volume for the branches or fascicles 8 of the nerve 6 terminals to grow.

[0088] For example, over a period of several months, the nerve fascicles 8 can reinnervate the free tissue graft 10 and grow nerve fibers 12 to find new neural targets. Once the free tissue graft 10 has been reinnervated, action potentials traveling along the nerve from the neuron then generate muscle level signal amplitudes, rather than nerve level amplitudes. In this way, the free tissue graft 10 (e.g., a free muscle graft) acts as an amplifier to the signals generated by the branches or fascicles 8 of the nerve 6 terminals, where the signals from the individual nerve fascicles 8 have voltage amplitudes that are greater than or equal to about 150 µV pp, in some examples, greater than or equal to about 250 µV pp or 500 µV pp, and, for example, up to about 1000 µV pp or more.

[0089] Although the neural interface system 4 can be used with any damaged, severed, or injured portion (e.g., nerve endings) of a nerve within an object, it is particularly well-suited for use with peripheral nerves. Therefore, the neural interface system 4 can be used with injured or damaged peripheral nerves, such as those involved in amputation. However, the methods described herein can also be used with a wide variety of different nerves. Therefore, in some respects, while the methods of this disclosure are particularly useful for peripheral nerves, the discussion of peripheral nerves and peripheral nerve interface devices is merely exemplary and not limiting.

[0090] like Figure 1 As shown, the free tissue graft 10 may be configured with an electrical conductor, such as electrode 14, in electrical communication with the free tissue graft 10. As described in more detail below, electrode 14 is in turn in electrical communication with a wire 18a, which in turn is in electrical communication with an implantation device 20, which includes processing circuitry 22 and an amplifier 24. In this case, signals from the nerve bundle 8 are received by electrode 14 and transmitted via wire 18a, for example, through amplifier 24, to the processing circuitry 22 of implantation device 20. Alternatively, electrode 14 may be omitted, and the electrical conductor in electrical communication with the free tissue graft 10 may be wire 18b placed directly in or on the free tissue graft 10. In this case, signals from the nerve bundle 8 are received by wire 18b itself through direct or indirect electrical communication with the free tissue graft 10 and transmitted via wire 18b, for example, through amplifier 24, to the processing circuitry 22 of implantation device 20. As an alternative, the electrical conductor communicating with the free tissue graft 10 can be a wire mesh 17 with multiple electrode sites and multiple conductive channels, placed in or on the free tissue graft 10. The wire mesh 17 is in turn in electrical communication with multiple wires 18c, which communicate with the implantation device 20. In this case, signals from the nerve bundle 8 are received by the wire mesh 17 and transmitted via the wires 18c, for example, through an amplifier 24, to the processing circuitry 22 of the implantation device 20. The amplifier 24 is a high-impedance amplifier. Furthermore, although... Figure 1 A single amplifier 24 is shown, but the implantable device 20 may also include additional amplifiers, including additional / separate amplifier circuitry for one or more individual nerve bundles 8 or groups of bundles 8. Alternatively, multiple implantable devices 20 or implantable devices with additional processing circuitry 22 may be used. As described herein, the neural interface system 4 may be an implantable neural interface device or an RPNI device, which typically includes: a free tissue graft 10; associated leads 18a, 18b, 18c; electrodes 14 or a wire mesh 17 with multiple electrodes (if present); and an implantable device 20 with processing circuitry 22.

[0091] As described in the present disclosure, the implanted device 20, for example, can be an implantable medical device similar to an automatic cardiac defibrillator implanted within a subject, but having processing capabilities to receive, process, record, and / or transmit the neural signals from the free tissue graft 10. Because the signals from the individual nerve fascicles 8 are amplified by the free tissue graft 10 (e.g., the free muscle graft) to a level of, for example, greater than or equal to about 150 µV pp or higher, the electronics contained within the implanted device 20 are smaller, less costly, require less processing power, and / or consume less battery power than the electronics required to adequately and meaningfully receive, record, and process the neural signals detected by prior systems, which, as described above, are typically less than 100 µV pp when received from within the nerve and less than 10 µV pp when received from a cuff around the nerve. Moreover, because the signals from the individual nerve fascicles 8 are amplified by the free tissue graft 10 to a level of, for example, greater than or equal to about 150 µV pp or higher, these signals are less susceptible to noise and interference, have a higher signal-to-noise ratio, and more accurately represent and correspond to the actual neural signals produced by the individual nerve fascicles 8. For example, the signal-to-noise ratio of these signals can be 4 or higher. Notably, the electrical signals at this level can be generated by the implantable neurointerface system 4, which in certain aspects, essentially comprises: the free tissue graft 10 and one or more electrical conductors (e.g., the leads 18a, 18b, 18c, the electrodes 14, and / or the wire mesh 17); and one or more portions of the nerve 6 that are regenerated and neuroreinnervated in the free tissue graft.

[0092] In certain aspects, a method of amplifying a neural signal in a subject includes placing a portion of a nerve 6 (e.g., a nerve bundle 8) within a free tissue graft 10 and securing the portion of the nerve 6 (the nerve bundle 8) therein. The free tissue graft 10 can be attached to the nerve bundle 8 via suturing, gluing, strapping, or other suitable attachment method or mechanism, for example. At least one electrical conductor (e.g., the electrode 14, the conductive wires 18a, 18b, 18c, and / or the wire mesh 17) can then be introduced into the free tissue graft 10. It is noted that the at least one electrical conductor can be introduced into the free tissue graft prior to securing the portion or branch of the nerve to the free tissue graft. The at least one electrical conductor provides electrical communication with the nerve 6. The electrical conductor can have a maximum thickness less than or equal to about 5 millimeters (mm). Thus, one or more portions of the nerve 6 (the nerve bundle 8) regenerate within the free tissue graft that reinnervates the tissue. This reinnervation can include the generation of sprouting nerve fibers 12. In this manner, the nerve 6 is thus able to generate an amplified electrical signal greater than or equal to about 150 microvolts without any external electrical input as described above. Notably, the ability to amplify electrical signals from a nerve as well as to generate electrical signals from a nerve reflect the subject's autonomous, spontaneous electrical signal generation at high voltage levels that was not previously possible. This autonomous, spontaneous electrical signal (e.g., naturally generated electrical signal from a motor nerve) can be distinguished from a stimulated nerve signal generated by introducing an external electrical input to the nerve for activation (e.g., stimulation by a combined compound action potential (CMAP) resulting from external nerve activation).

[0093] In certain other aspects, the method can include cutting a portion of a nerve in the subject (e.g., cutting a nerve terminal) to produce one or more branches or bundles. In certain aspects, the cutting can include cutting the nerve terminal into a plurality of portions, such as branches / bundles. Thus, for each respective portion of the nerve, the placing of the nerve in the free tissue graft and the introduction of the electrical conductor into the free tissue graft assembly can be repeated. The method can also include obtaining the free tissue graft from tissue of the subject prior to processing the cut terminal. In certain aspects, the tissue is muscle tissue. In alternative aspects, the tissue can be dermal tissue. As will be discussed in greater detail below, in certain aspects, the free tissue graft has a maximum dimension less than or equal to about 10 centimeters (cm). In other aspects, the free tissue graft has a maximum dimension less than or equal to about 5 cm.

[0094] In some aspects, the method according to certain aspects of this disclosure may also include stimulating one or more portions (e.g., branches / bundles) of a nerve using stimulation signals transmitted via one or more electrical conductors in electrical communication with the free tissue graft. This provides the ability to transmit perceptual feedback via stimulation to the brain of the subject through the neural interface system 4.

[0095] refer to Figure 2 This shows three free tissue grafts 10, which were obtained from the object but before being surgically attached to the object's nerve bundles. Figure 2 It also includes a photograph 28 showing the surgical procedure for attaching a free tissue graft 10 to a nerve bundle of a subject via surgery. Further description of attaching a free tissue graft 10, such as a muscle graft (also referred to as an autologous graft of a free transplanted autologous muscle tissue block from a subject) to a nerve bundle via surgery is provided in U.S. Publication 2013 / 0304174, which was published on November 14, 2013 and is jointly assigned. The entire disclosure of U.S. Publication 2013 / 0304174 is incorporated herein by reference.

[0096] Since the free tissue graft 10 (e.g., a muscle graft) can be surgically obtained from non-essential donor muscle within the recipient, the free tissue graft 10 undergoes complete denervation after acquisition, thereby terminating the previously existing nerve innervation within the free tissue graft 10. As described above, this acquisition process also causes devascularization of the native cells of the free tissue graft 10. Once the free tissue graft 10 is surgically attached to the nerve bundle 8, the free tissue graft 10 undergoes a process of nerve renervation, whereby the attached nerve bundle 8 renervates the free tissue graft 10 and grows nerve fibers 12, which grow within the free tissue graft 10 to seek new neural targets. After the prior denervation process, signals from the newly attached nerve bundle 8 and the newly grown nerve fibers 12 do not need to compete with residual neural signals from the nerve bundles and nerve fibers that previously innervated the free tissue graft 10.

[0097] Further, once reattached to the subject via surgery, the free tissue graft 10 can obtain nutrients via a process of plumping, rather than simply dying and being reabsorbed by the subject's body. As such, if the free tissue graft 10 is within an optimal volume / size range, the free tissue graft 10 can absorb nutrients and blood from the surrounding tissue and fluid to support the process of reinnervation, even without a natural blood supply. Eventually, as the free tissue graft 10 re-integrates with the subject's body, a new blood supply network can be established. This process of reinnervating the free tissue graft 10 with newly grown nerve fibers 12 from the attached nerve bundle, following the denervation process of the free tissue graft 10, is combined with the processes of plumping and revascularization, resulting in a region of muscle or other tissue from which the implant device 20 can receive highly specialized electrical signals from the individual nerve bundle 8, for example, greater than or equal to about 150 μν pp or higher.

[0098] As mentioned above, to facilitate the process of reinnervation and plumping, the free tissue graft 10 is preferably within an optimal volume / size range. For example, the volume / size of the free tissue graft 10 can be selected to be small enough to revascularize quickly via collateral blood flow, while providing a large enough area or volume for the nerve to grow in without forming an unorganized neuroma. In certain preferred aspects, the free tissue graft 10 can have a maximum dimension less than or equal to about 10 cm. For example, in certain variations, the free tissue graft 10 can have a maximum dimension less than or equal to about 10 cm in any direction. For example, in certain variations, the free tissue graft 10 can have a length less than or equal to about 10 cm, or more preferably, less than or equal to about 5 cm. Further, the free tissue graft 10 can have a width less than or equal to about 10 cm, or more preferably, less than or equal to about 5 cm. The free tissue graft 10 can optionally have a thickness less than or equal to about 2 cm to 3 cm. Further, the optimal dimensions of the free tissue graft 10 can include a length less than or equal to about 5 cm, and a diameter greater than or equal to about 2 cm to less than or equal to about 3 cm. For example, the preferred optimal dimensions of the free tissue graft 10 can include a length of about 3.5 cm, and a diameter of about 2 cm. It should be noted that the free tissue graft 10 can have a variety of different dimensions and / or geometries, and those described herein are exemplary. Additionally, a discussion of the dimensions of a free graft of autologous muscle tissue from a subject is included, for example, in paragraphs

[0082] to

[0088] of U.S. Pub. 2013 / 0304174, published November 14, 2013, which is incorporated herein by reference in its entirety.

[0099] ReferringFigure 3 , showing an example embodiment 100 having multiple free tissue grafts 10 (e.g., free muscle grafts) and electrodes 14 connected to an implant device 20. The implant device, in addition to receiving signals from individual nerve fascicles through the free tissue grafts 10 and electrodes 14, also controls an end device, which in this case is a prosthetic hand 110. Specifically, as shown, Figure 3 each of the radial nerve 102, median nerve 104, and ulnar nerve 106 has been split into multiple individual nerve fascicles, which have been attached to corresponding free tissue grafts 10, and are in electrical communication with the processing device 22 of the implant device 20 through electrical communication with the electrodes 14.

[0100] As discussed in greater detail below, the processing circuit 22 of the implant device 20 monitors the signals from the individual fascicles and, based on analysis of the received signals, controls, for example, flexion and extension of the prosthetic hand 110. For example, as discussed in greater detail below, training data can be obtained through a calibration procedure in which a subject is asked to perform certain actions while the neural signals are monitored and recorded by the processing circuit 22 of the implant device 20 and transmitted to an external computing device such as a desktop or laptop computer. The training data set is then analyzed and used to estimate parameters for the processing circuit 22 to use in driving the prosthetic hand 110, which are then downloaded from the external computing device to the processing circuit 22 of the implant device 20. For example, as shown, Figure 3 the implant device 20 is in communication with actuators 112, which, as described in greater detail below, drive flexion and extension of individual fingers of the prosthetic hand 110.

[0101] Referring to Figure 4 , in addition to sensing or reading signals generated by the nerve fascicles 8, amplified by the free tissue grafts 10, the RPNI device of the present disclosure can also be used to stimulate individual nerve fascicles 8 or individual nerve fibers. For example, as shown, Figure 4 the free tissue grafts 10 are configured with three electrodes 30a, 30b, 30c, which are in communication with the processing circuit 22 of the implant device 20 through amplifiers 32, 32b, 32c and wires 34a, 34b, 34c. In this way, as discussed in greater detail below, the processing circuit 22 can stimulate individual nerve fascicles 8, for example, using negative voltage stimulation signals or positive voltage inhibition stimulation signals. Generally, negative voltage signals will cause a nerve to fire, while positive voltage will inhibit a nerve from firing.

[0102] Existing clinical applications, such as vagus nerve stimulation, typically use a cuff that wraps around the entire nerve. In this way, a large portion of the nerve is typically stimulated. However, using, for example, the RPNI device of the present disclosure, Figure 4The RPNI device shown, the processing circuitry 22 can process stimulation of a particular fascicle by directing signals to the electrodes 30a, 30b, 30c via the wires 34a, 34b, 34c. More specifically, the use of the free tissue graft 10 can allow a single fascicle 8, which can be about 1 mm in diameter, to be expanded into a structure that is 1 cm by 3.5 cm for the purposes of stimulation. Stimulation of the entire structure (i.e., the entire free tissue graft 10) can specifically address the corresponding single fascicle 8.

[0103] Furthermore, by using multiple electrical contacts (e.g., multiple electrodes), stimulation of more specific areas (e.g., a small portion of a fascicle 8 or an individual fiber) can be achieved using current steering. For example, when stimulation is performed with only a single negative contact, the negative voltage can spread and dissipate. Conversely, by using current steering, the negative voltage can be surrounded by positive voltage to concentrate the negative voltage at a single location. Continuing with the example above, Figure 4 A negative voltage can be applied with the electrode 30b, while positive voltages can be applied with the electrodes 30a and 30c, in order to provide more focus to the location from which the negative voltage is applied with the electrode 30b.

[0104] As discussed in more detail below, for example, the nerve stimulation can be used to sense a prosthesis response to pressure sensed by a pressure sensor of the prosthesis to stimulate a nerve. Additionally, the nerve stimulation can be used to inhibit a pathological pain signal. Additionally, the nerve stimulation, for example, can be used to inhibit pathological contractions of the bladder. Additionally, the nerve stimulation can be used for sphincter control, erectile dysfunction, and / or to control nerves associated with internal organs such as the liver, adrenal glands, stomach, pancreas, and kidneys. For example, such nerve stimulation can be used on a renal artery to interfere with and treat abnormal nerve signals in the kidney that can otherwise cause hypertension.

[0105] Referring to Figure 5 , further details of the implanted device 20 are shown. As discussed above, the implanted device 20 can receive the amplified nerve signal input 52 from the free tissue graft 10 through the wires 18a, 18b, 18c (as shown). As discussed further above, the implanted device 20 can transmit the nerve stimulation signal output 54 through the wires 34a, 34b, 34c (as shown) to stimulate a nerve with the electrodes 30a, 30b, 30c. As discussed further above, the implanted device 20 can transmit the prosthesis control signal output 56 to control movement of a prosthetic device. For example, the implanted device 20 can transmit the prosthesis control signal output 56 via a data bus such as a controller area network (CAN) bus to control a prosthetic device (as shown). Figure 1 Figure 4 Figure 3 ​​(As shown) the flexion and extension of the prosthetic hand 110. For example, the implantation device 20 can transmit prosthetic control signal output 56 to control the flexion and extension of the prosthetic hand (such as...). Figure 3 (As shown) Each actuator 112. Furthermore, as further discussed above, for example, the implantable device 20 can receive prosthetic sensor input signals 58 generated by one or more pressure sensors, these prosthetic sensor input signals corresponding to pressure sensed by pressure sensors of the prosthesis. As described above, the implantable device 20 can generate a neural stimulation signal output 54 based on the pressure sensor signal input from the pressure sensors of the prosthesis received by the implantable device.

[0106] like Figure 5 As shown, the implantable device includes processing circuitry 22, communication circuitry 50, and memory 62, both of which communicate with processing circuitry 22. Communication circuitry 50 enables the implantable device (and particularly the processing circuitry 22 of implantable device 20) to communicate wirelessly with computing devices external to implantable device 20, such as external computing devices like desktop or laptop computers. In this way, processing circuitry 22 can transmit data, such as amplified neural signal input data received via amplified neural signal input 52. This communication can be used during calibration processes to receive training and calibration data from implantable device 20 at an external computing device for viewing and analysis, and to transmit estimated operating parameters and configuration data by implantable device 20, for example, for driving a prosthesis or generating neural stimulation signal outputs. Communication circuitry 50 may include an antenna and a receiver and transmitter, or transceiver, for communication via radio frequency (RF) 60. For example, communication circuit 50 can communicate via wireless protocols, such as the CEN ISO / IEEE 11073 communication protocol used for communication between medical devices and external information systems. Alternatively, communication circuit 50 can communicate via, for example, WiFi. ® or Bluetooth ® It communicates using other wireless protocols.

[0107] The memory 62 can be used by the processing circuitry 22 to store, for example, amplified neural signal input data received via amplified neural signal input 52 prior to communication with an external computing device via the communication circuitry 50. The memory 62 can also be used to store estimated operating parameters and configuration data received from an external computing device and used by the implanted device during operation. The memory 62 can also be used by the processing circuitry 22 to store event or operational history data, or any other data associated with various inputs and outputs received or generated by the processing circuitry 22.

[0108] refer to Figure 6, showing a hardware device chip 64 that can include or be used to implement, for example, the processing circuitry 22, the communication circuitry 50, and the memory 62 of the implanted device (as shown in Figure 5 ). The hardware device chip 64 can also include or be used to implement the amplifier 24 (as shown in Figure 1 ) and the amplifiers 32a, 32b, 32c (as shown in Figure 4 ). The hardware device chip 64 can preferably include a high input impedance bioamplifier configured to process received high output impedance bio signals from the free tissue graft 10. In addition, the hardware device chip 64 can preferably include functionality to reject larger common mode signals, such as recording from a pair of electrodes in differential fashion, referencing received signals to a local reference in the vicinity of the one or more RPNI devices, or using a stronger high pass filter in the first stage. The hardware device chip 64 can preferably include an amplifier to amplify received signals from the nerve bundle 8, for example, by a factor of 1000. Preferably, the hardware device chip 64 has very low noise, but this feature can be less important given the relatively high amplitude of the received amplified nerve signal input from the nerve bundle 8. In addition, the hardware device chip 64 can preferably include a band pass filter to filter received amplified nerve signal input between 10000 Hz and 2000 Hz before further processing. In addition, the hardware device chip 64 can preferably take the absolute value of the received amplified nerve signal input in the analog domain.

[0109] Referring to Figure 7, showing further details of the processing circuitry 22, which is shown in communication with the communication circuitry 50 and the memory 62 of the implant device 20. The processing circuitry 22 includes a neural input signal conditioning circuitry 70 for receiving and conditioning the amplified neural signal input 52 from the nerve fascicles received through the free tissue graft 10. The function and operation of the neural input signal conditioning circuitry 70 is discussed in greater detail below. The processing circuitry 22 also includes a neural input signal decoding circuitry 72 for processing and decoding the signal data conditioned by the neural input signal conditioning circuitry 70. The function and operation of the neural input signal decoding circuitry 72 is discussed in greater detail below. The processing circuitry 22 also includes a neural stimulation signal output circuitry 74 for generating the neural stimulation signal output 54. The function and operation of the neural stimulation signal output circuitry 74 is discussed in greater detail below. The processing circuitry 22 also includes a prosthesis control circuitry 76 for generating the prosthesis control signal output 56 for controlling the prosthetic limb. The function and operation of the prosthesis control circuitry 76 is discussed in greater detail below. The prosthesis control signal output 56 for controlling the prosthetic limb can be transmitted to the prosthetic limb via a data bus such as a CAN bus. The processing circuitry 22 also includes a prosthesis sensor receiver circuitry 78 for receiving the prosthesis sensor signal input 58 from the pressure sensor of the prosthetic limb. The function and operation of the prosthesis sensor receiver circuitry 78 is discussed in greater detail below.

[0110] Reference is made to Figure 8 , showing a control algorithm 800 for receiving and recording the amplified neural signal data from the free tissue graft 10. The control algorithm 800 can be executed by the processing circuitry 22 of the implant device 20. More specifically, the control algorithm 800 can be executed at least in part by the neural input signal conditioning circuitry 70 (as shown in Figure 7 ) of the processing circuitry 22. The control algorithm 800 begins at 802.

[0111] At 804, the neural input signal conditioning circuitry 70 of the processing circuitry 22 receives one or more amplified neural signals from the conductors in electrical communication with one or more free muscle grafts. As described in detail above with reference to Figure 1 , the electrical conductors can be the electrodes 14, wire mesh 17, or lead 18b in electrical communication with the free tissue graft 10. As described further above, the voltage amplitude of the electrical signals can be greater than or equal to about 150 µV pp, and in some examples, greater than or equal to about 250 µV pp or 500 µV pp, and for example up to about 1000 µV pp or more. Further, in examples where the free tissue graft 10 is a nerve graft, the electrical signals can be action potentials. In examples where the free tissue graft 10 is a muscle graft, the electrical signals can be muscle action potentials. In examples where the free tissue graft 10 is a nerve graft, the electrical signals can be action potentials. In examples where the free tissue graft 10 is a muscle graft, the electrical signals can be muscle action potentials. Figure 1In the embodiment of amplifier 24 shown, the signal can be amplified before being received by processing circuit 22. In this case, for example, the signal can be pre-regulated by amplifying it by 1000 times using amplifier 24 before being received by neural input signal conditioning circuit 70 of processing circuit 22.

[0112] In step 806, the neural input signal conditioning circuit 70 of the processing circuit 22 conditions the received signal from the free tissue graft 10 and extracts features from the received signal from the free tissue graft. For example, in cases where (e.g.) Figure 1 In the embodiment of amplifier 24 shown, the neural input signal conditioning circuit 70 can amplify the received signal, for example, by a factor of 1000. The neural input signal conditioning circuit 70 can then filter the received signal using a predetermined analog frequency range. For example, the predetermined analog frequency range could be between 10 Hz and 1000 Hz or between 10 Hz and 2000 Hz. After filtering, the neural input signal conditioning circuit 70 can digitize the filtered signal using a predetermined sampling rate. For example, the neural input signal conditioning circuit 70 can digitize the filtered signal using a sampling rate of 30,000 samples per second. The digitized signal can then be digitally filtered using a predetermined digital frequency range. For example, the predetermined digital frequency range could be between 100 Hz and 500 Hz. After digital filtering, the signal can then be downsampled to 1000 samples per second. Because the voltage amplitude of the initially received signal is relatively large, the conditioning and feature extraction performed in step 806 produce a more robust and well-defined signal that is less susceptible to noise or interference compared to systems that do not utilize the free tissue graft 10 to amplify the signal from the nerve bundle.

[0113] At 808, the processing circuit 22 records the obtained signal data in the memory 62 and / or transmits the obtained signal data to an external computing device using the communication circuit 50. For example, the obtained signal data can be stored in the memory 62 of the implanted device 20 and then transmitted to an external computing device via a batch processing communication process through the communication circuit 50. Alternatively, the memory 62 can be used as a buffer to receive and store the obtained signal data for further processing by the processing circuit 22 or for transmission to an external computing device via the communication circuit 50. Alternatively, the obtained signal data can be streamed to an external computing device in real time via the communication circuit 50.

[0114] After the obtained signal data is recorded or transmitted at 808, the processing circuit 22 loops back to 804 and continues to receive one or more amplified neural signals. Although the control algorithm 800 is shown as a series of consecutive steps for illustrative purposes, it should be understood that the individual steps can occur in parallel and continuously through the processing circuit 22 as the amplified neural signals are continuously and instantly received.

[0115] refer to Figure 9 This illustrates the use of signals received from the free tissue graft 10 to control, for example, (e.g.) Figure 3 The control algorithm 900 for prosthetic limbs such as the prosthetic hand 110 (shown) is described. The control algorithm 900 can be executed by the processing circuitry 22 of the implanted device 20. More specifically, the control algorithm 900 can be at least partially executed by the processing circuitry 22 (e.g., ...). Figure 7 The neural input signal conditioning circuit 70, neural input signal decoding circuit 72, and prosthesis control circuit 76 (shown) are executed. Control algorithm 900 starts at 902.

[0116] In 904, the neural input signal conditioning circuit 70 of processing circuit 22 receives one or more amplified neural signals from a conductor in electrical communication with one or more free muscle grafts. (See above for reference.) Figure 8 Step 804 describes the function of step 904, and will not be repeated here.

[0117] In step 906, the neural input signal conditioning circuit 70 of the processing circuit 22 modulates the received signal from the free tissue graft 10 and extracts features from the received signal from the free tissue graft. (See above for reference.) Figure 8 Step 806 describes the function of step 906, and will not be repeated here.

[0118] At 908, the neural input signal decoding circuit 72 decodes the obtained signal data to determine, for example, whether the obtained signal data corresponds to the flexion or extension of the prosthesis. Although Figure 9 The control algorithm 900 is described as decoding the obtained signal data used for flexion or extension movements; however, it should be understood that, where appropriate, other prosthetic movement controls can be similarly decoded from the obtained signal data. The neural input signal decoding circuit 72 can use a one-of-two classifier (e.g., a Naive Bayes classifier) ​​or regression analysis to determine whether the obtained signal data in predetermined time intervals, such as 25 milliseconds (ms), indicates flexion or extension. The following references... Figure 10 The control algorithm 1000 shown for decoding signals to control the prosthesis describes further details of decoding the obtained signal data.

[0119] At 910, processing circuit 22 determines whether the obtained signal data within a predetermined time period corresponds to flexion or extension of the prosthesis. At 910, when the obtained signal data corresponds to extension, the processing circuit proceeds to 912, and the prosthesis control circuit 76 of processing circuit 22 drives the prosthesis in the extension direction. At 910, when the obtained signal data corresponds to extension, the processing circuit proceeds to 914, and the prosthesis control circuit 76 of processing circuit 22 drives the prosthesis in the flexion direction. For example, in... Figure 3 In the case of the prosthetic hand 110 shown, the processing circuit 22 can drive the actuator 112 of the prosthetic hand 110 in either the flexion or extension direction, if appropriate. After the prosthesis is driven at step 912 or 914, the processing circuit 22 cycles back to 904.

[0120] refer to Figure 10 The diagram illustrates a control algorithm 1000 for decoding signals to control the prosthesis. The control algorithm 1000 can be executed by the processing circuitry 22 of the implanted device 20. More specifically, the control algorithm 1000 can be executed at least in part by the neural input signal decoding circuitry 72. Figure 10 The function of the control algorithm 1000 shown is in Figure 9 The process is summarized in step 908. Control algorithm 1000 begins at point 1002.

[0121] At step 1004, the neural input signal decoding circuit 72 determines whether the current sample group within a predetermined time segment is complete. For example, the predetermined time segment could be 25 ms, and the sampling interval could be 1 ms. In this case, the neural input signal decoding circuit 72 can wait in step 1004 until a complete sample group of 25 samples at 1 ms intervals is formed. When the sample group is not yet complete, the neural input signal decoding circuit 72 loops back to step 1004. When the sample group is complete, the neural input signal decoding circuit 72 proceeds to step 1006.

[0122] In step 1006, the neural input signal decoding circuit 72 uses a binary-choice classifier to classify each sample in the sample group. For example, when the neural input signal decoding circuit 72 is decoding the obtained signal data used for flexion or extension, it can classify each sample in the sample group as a flexion sample or an extension sample. For example, the neural input signal decoding circuit 72 can use a binary-choice Naive Bayes classifier or regression analysis to classify each sample in the sample group as a flexion sample or an extension sample.

[0123] The Naive Bayes classifier can use training data previously collected from the subject during the calibration process and routine. For example, the subject can be commanded to perform a flexion action or an extension action, and the resulting neural signal data can be recorded by the processing circuit 22 and transmitted to the external computing device for analysis. Based on the training data collected, a Gaussian distribution for each of the flexion and extension motions can be estimated or computed based on the received neural signal data. For example, the Gaussian distributions for the flexion and extension motions will then have different means and variances. The parameters and data for the Naive Bayes classifier can be estimated by the external computing device based on the training data collected, and then transmitted to the processing circuit 22 and stored in the memory 62 for use by the neural input signal decoding circuit 72 in decoding the neural signal data.

[0124] During step 1006, the neural input signal decoding circuit 72 can compare each sample within the sample set to the previously determined Gaussian distributions for the flexion and extension motions having different means and variances, and compute the probability that the particular sample was drawn from each of the two distributions. Each sample is then classified based on which of the two motions has a higher probability for the particular sample. For example, if the particular sample has a higher probability that it corresponds to a flexion motion, the sample is classified as a flexion sample. If the particular sample has a higher probability corresponding to an extension motion, the sample is classified as an extension sample. Once all of the samples within the sample set have been classified, the neural input signal decoding circuit 72 proceeds to 1008.

[0125] At 1008, the neural input signal decoding circuit 72 determines whether there are more flexion samples or more extension samples in the particular sample set, and classifies the entire sample set based on the determination. For example, when there are more flexion samples in the sample set, the sample set is classified as a flexion sample set, and when there are more extension samples in the sample set, the sample set is classified as an extension sample set. In this way, the neural input signal decoding circuit 72 predicts whether a set of samples in a particular sample set is indicative of, for example, a flexion motion or an extension motion. It will be appreciated that other motions can similarly be included in the classification and prediction process. After the sample set has been classified, the neural input signal decoding circuit 72 loops back to 1004.

[0126] In this way, reference is made to Figure 9 and Figure 10Both, the processing circuit 22 can determine or predict, for example, whether the neural signal data within a particular predetermined time segment (e.g., 25 ms) corresponds to or indicates a flexion or extension movement. Further, the prosthesis control circuit 76 can send control commands to the prosthesis every 25 ms based on the classification of the current or most recent sample set. In this way, the processing circuit 22 can continuously monitor and decode the neural signal data and send corresponding commands to operate the prosthesis. Although the above example is described using a predetermined time segment, it should be understood that shorter or longer predetermined time segments can be used.

[0127] Referring to Figure 11 , an example plot of neural signal data over a 5 second time period is shown having a corresponding to the start of a flexion movement. Similarly, referring to Figure 15 , an example plot of neural signal data over a 400 millisecond time period is shown having a corresponding to the start of a finger flexion movement.

[0128] As described above with reference to Figure 9 and Figure 10 , the training data collected from the subject during the calibration routine can be used to generate, for example, a mapping of received neural signals to corresponding prosthesis movements or prosthesis actions. In the example of a transradial amputee, individual nerve bundles can map well to individual hand muscles simulated with the prosthesis. In the example of a transhumeral amputee, the training data can be used to determine which nerve bundles or which groups of neural signal inputs are most relevant to which individual hand muscles simulated with the prosthesis.

[0129] Additionally, neural signal data (e.g., average signal power, number of zero-crossing events, or count of detected spikes) can also be monitored, recorded, and analyzed for each signal from each nerve bundle and used, for example, to calculate a desired velocity for all five fingers of a prosthetic hand to send in a single command at each time step (e.g., every 25 ms). There is not a one-to-one correspondence between a particular muscle and the velocity of an individual finger. For example, for flexing only the little finger, the subject can need to simultaneously extend the index finger. Thus, finger velocities can be regressed from muscle activity on all neural signal channels to determine a consistent overall mapping. Various algorithms can be used to estimate instantaneous velocity from various signals, including, for example, linear filters, Kalman filters, and particle filters.

[0130] In addition, linear discriminant analysis, Naive Bayes classifier, or support vector machines can be used to predict individual discrete states, such as grasping and pointing. In each case, a training data set is obtained through a calibration process by asking the subject to perform various motions or actions and using the implanted device 20 and processing circuitry 22 to monitor and record the resulting neural signal data. The training data set can then be used to estimate operating parameters used by the processing circuitry 22 and, for example, the prosthesis control circuitry 76 to control operation of the prosthetic hand. The estimated operating parameters can then be downloaded to the processing circuitry 22 through the communication circuitry 50 and stored in the memory 62 for use by the processing circuitry 22, for example, to make instantaneous estimates of finger velocity to drive the prosthetic hand.

[0131] Referring to Figure 12 , a control algorithm 1200 for monitoring a neuropathic pain signal of a nerve is shown. The control algorithm 1200 can be executed by the processing circuitry 22 of the implanted device. More specifically, the control algorithm 1200 can be executed at least in part by the neural input signal conditioning circuitry 70, the neural input signal decoding circuitry 72, and the neural stimulation signal output circuitry 74. The control algorithm 1200 begins at 1202.

[0132] At 1204, the neural input signal conditioning circuitry 70 of the processing circuitry 22 receives one or more amplified neural signals from conductors in electrical communication with one or more free muscle grafts. The function of step 1204 is described above with reference to step 804 in Figure 8 , which is not repeated here.

[0133] At 1206, the neural input signal conditioning circuitry 70 of the processing circuitry 22 conditions the received signals from the free tissue graft 10 and extracts features from the received signals from the free tissue graft. The function of step 1206 is described above with reference to step 806 in Figure 8 , which is not repeated here.

[0134] At 1208, the neural input signal decoding circuitry 72 decodes the resulting signal data to determine whether the resulting signal data, for example, is indicative of a neuropathic pain signal. The decoding performed at 1208 is similar to the decoding described above with reference to steps 908 and Figure 9 , and steps 1004 through 1008 in Figure 10 , which describe decoding the resulting signal data to determine whether the resulting signal data is indicative of a flexion action or an extension action. The decoding performed at 1208 is similar to the decoding described above with reference to steps 1004 through 1008 in Figure 9 and Figure 10The decoding described is similar; the decoding performed at 1208 can be based on the training dataset collected during the calibration process with the object, similarly using a binary classifier such as a Naive Bayes classifier to determine whether the obtained signal data corresponds to a state that is generating pathological pain signals. After decoding the obtained signal at 1208, the processing circuit 22 proceeds to 1210.

[0135] At 1210, processing circuit 22 determines whether a pathological pain signal has been detected based on decoding the obtained signal data. When a pathological pain signal is detected, processing circuit 22 proceeds to 1212 and stimulates the appropriate nerve bundle with inhibitory stimulation. Specifically, the nerve stimulation signal output circuit 74 of processing circuit 22 can stimulate the appropriate nerve bundle with a positive voltage to inhibit nerve activity and suppress or reduce the activity of pathological pain signals in that nerve bundle. In this way, pain signals in the body can be relieved without permanently losing the perception of a specific nerve or nerve bundle at the site of tissue. At 1212, after stimulating the nerve with inhibitory stimulation, or at 1210, after determining that no pathological pain signal has been detected, processing circuit 22 loops back to 1204.

[0136] refer to Figure 13 A control algorithm 1300 for monitoring pathological bladder contraction signals is shown. The control algorithm 1300 can be executed by the processing circuitry 22 of the implanted device. More specifically, the control algorithm 1300 can be executed at least in part by the neural input signal conditioning circuitry 70, the neural input signal decoding circuitry 72, and the neural stimulation signal output circuitry 74. The control algorithm 1300 begins at 1302.

[0137] At 1304, the neural input signal conditioning circuit 70 of the processing circuit 22 receives one or more amplified neural signals from a conductor in electrical communication with one or more free muscle grafts. (See above reference.) Figure 8 Step 804 describes the function of step 1304, and will not be repeated here.

[0138] At 1306, the neural input signal conditioning circuit 70 of the processing circuit 22 modulates the received signal from the free tissue graft 10 and extracts features from the received signal from the free tissue graft. (See above for reference.) Figure 8 Step 806 describes the function of step 1306, and will not be repeated here.

[0139] At 1308, the neural input signal decoding circuit 72 decodes the obtained signal data to determine whether the obtained signal data indicates, for example, a pathological bladder contraction signal. The decoding performed at 1308 is similar to that described above. Figure 9 Step 908 andFigure 10 The decoding described in steps 1004 to 1008 describes decoding the obtained signal data to determine whether the obtained signal data indicates a flexion or extension movement. (Refer to the above reference) Figure 9 and Figure 10 The decoding described is similar; the decoding performed at 1308 can be based on the training dataset collected during the calibration process with the object, similarly using a binary classifier such as a Naive Bayes classifier to determine whether the obtained signal data corresponds to a state that is generating pathological bladder contraction signals. After decoding the obtained signal at 1308, the processing circuit 22 proceeds to 1310.

[0140] At 1310, processing circuit 22 determines whether a pathological bladder contraction signal has been detected based on decoding the obtained signal data. When a pathological bladder contraction signal is detected, processing circuit 22 proceeds to 1312 and stimulates the appropriate nerve bundle with inhibitory stimulation. Specifically, the nerve stimulation signal output circuit 74 of processing circuit 22 can stimulate the appropriate nerve bundle with a positive voltage to inhibit nerve activity and suppress or reduce the activity of the pathological bladder contraction signal in that nerve bundle. At 1312, after stimulating the nerve with inhibitory stimulation, or at 1310, after determining that no pathological pain signal has been detected, processing circuit 22 loops back to 1304.

[0141] Although reference Figure 13 The described control algorithm 1300 is described in the context of monitoring and inhibiting pathological bladder contraction signals, but this control algorithm can be similarly applied to other applications. For example, control algorithm 1300 can be suitably applied to sphincter control or erectile dysfunction. Similarly, control algorithm 1300 can be applied, for example, to control nerves associated with visceral organs such as the liver, adrenal glands, stomach, pancreas, and kidneys. In each case, training data is collected from the subject and analyzed to generate appropriate monitoring parameters, which are then downloaded to the implanted device 20. The implanted device 20 then monitors neural signal activity to determine whether neural stimulation is appropriate.

[0142] refer to Figure 14 This illustrates a control algorithm 1400 for stimulating nerves based on sensed pressure signals from a prosthetic device. For example, such as... Figure 3The prosthetic device, such as the prosthetic hand 110 shown, can be equipped with pressure sensors, for example, located in the fingertips of the prosthetic. The pressure sensors can sense pressure and transmit pressure signals back to the processing circuit 22 of the implanted device 20. The control algorithm 1400 can be executed by the processing circuit 22 of the implanted device. More specifically, the control algorithm 1400 can be executed at least in part by the prosthetic sensor receiver circuit 78 and the neural stimulation signal output circuit 74. The control algorithm 1400 begins at 1402.

[0143] At 1404, the prosthetic sensor receiver circuit 78 receives pressure signals from the pressure sensors of the prosthetic, which correspond to the pressure sensed at the location of the pressure sensors. At 1406, the neural stimulation signal output circuit 74 stimulates individual nerve fascicles based on the received sensed pressure signals. A calibration process on the subject can be used to generate training data to determine which individual nerve fascicles should be most appropriately mapped to which pressure sensors. Further, the rate or level of discharge of the stimulation can correspond to the level of pressure sensed by the pressure sensors. In this way, the implanted device can transmit haptic feedback signals from the prosthetic to the appropriate nerve fascicles.

[0144] The following specific examples are provided for the purpose of illustrating how the compositions, devices, and methods of the present technology can be made and used, and are not intended to represent that a given embodiment of the present technology has been made or tested, or that a given embodiment of the present technology is the only one that can be made or tested.

[0145] Examples Example 1 RPNI study on non-human primates In the following example, RPNI were implanted in the forearms of two non-human primates (monkey R and monkey L) through surgery. Specifically, monkey R was implanted with three RPNI and monkey L was implanted with four RPNI. Muscle grafts were attached to small branches of the median and radial nerves to provide independent finger flexion / extension signals and thumb flexion signals. The surgery followed a standard procedural checklist, and the animals were monitored daily in their cages for ten days post-surgery, and then observed on a primate chair in daily experiments thereafter.

[0146] No major complications were found, and the animals recovered normal use of the limbs within a week after surgery. In a second surgery on both animals, the muscle grafts were observed to have significant revascularization. The RPNI responded to electrical stimulation with large amplitude compound muscle action potentials (CMAPs), which indicated reinnervation of the muscle grafts by the implanted nerve fascicles.

[0147] In the third surgery on monkey L, four bipolar "IM-MES" intramuscular electrodes manufactured by Ardiem Medical were implanted in two matured RPNI and in a healthy, intact muscle for comparison (extensor carpi radialis brevis (ECRB), a wrist extensor muscle). One electrode was placed in the muscle graft of a newly created RPNI construct, which was then matured over three months to generate high amplitude signals. The presence of the electrodes did not negatively affect the regeneration, reinnervation, and maturation of the RPNI during the maturation phase. The electrode leads were tunneled subcutaneously from the monkey's forearm to the back, where they exited through the skin to be connected to the recording equipment. Daily recordings from the implanted electrodes were made during task performance. The percutaneous site with the exiting leads was gently cleaned with betadine solution every week, and no infection was found. The site looked clean, was minimally irritating, and did not cause any apparent discomfort to the animal.

[0148] Reference Figure 16 Graph 1600 shows the autonomous RPNI signals recorded by the semi-long term Ardiem IM-MES electrodes in vivo in monkey L in μV, and the calculated percentage of flexion corresponding to the RPNI signals. Graph 1602 shows the spontaneous RPNI signals recorded by the percutaneous thin filament electrodes in vivo in monkey L in μV, and the calculated percentage of flexion corresponding to the RPNI signals. Graph 1604 shows the spontaneous RPNI signals recorded by the percutaneous thin filament electrodes in vivo in monkey R in μV, and the calculated percentage of flexion corresponding to the RPNI signals.

[0149] Referring to graph 1600, the signals recorded by the IM-MES electrodes varied from animal to animal and RPNI graft, with amplitudes in the range of 50 μV pp to 500 μV pp. Graphs 1600, 1602, 1604 show representative signals that look similar to sparse electromyographic (EMG) signals, which typically show multiple distinct single motor units. The far right portion of graph 1600 shows an amplified portion of the voltage signal that shows single muscle twitches. All observed putative single units corresponded reliably to flexion events, with a length of about 4 ms and variable firing frequency. The high signal to noise ratio (SNR) of the RPNI signals allowed for automatic detection of spontaneous RPNI activations with more than 95% accuracy using a linear discriminant classifier. The RPNI signals were used to control the prosthetic hand in real time when monkey L performed the behavioral task.

[0150] Example 2 RPNI studies on humans In the following example, three RPNI were implanted in a human body for the purpose of neuroma control by surgery. The patient had a distal radial amputation proximal to the wrist. Multiple muscle grafts of about 1 x 3 cm were taken from the surrounding tissue and sutured to the distal ends of the median, ulnar and radial nerves, respectively. At this level, the radial nerve (and thus the RPNI grafts) contained only sensory fibers that originally innervated the skin of the back of the hand. The median and ulnar nerves, as well as the RPNI, contained a mixture of sensory fibers that innervated the hand with motor fibers that originally innervated the intrinsic muscles of the hand. When the patient performed several hand movements, the electromyographic (EMG) activity of the median, ulnar and radial RPNI was recorded using percutaneous wire electrodes. As expected, the RPNI generated EMG in response to movements in which the muscles originally innervated by the severed nerves participated.

[0151] Reference Figure 17 The graphs 1700 and 1702 show the signals recorded from the median, ulnar and healthy wrist muscle (Flexor Carpi Ulnaris, FCU) during two different hand movements. Specifically, the graph 1700 shows the signals recorded during a thumb- little finger opposition action, which corresponds to the tip of the thumb touching the tip of the little finger. The graph 1702 shows the signals recorded during a thumb opposition action, which corresponds to the tip of the thumb touching the base of the little finger without flexing the little finger. These graphs show that physiologically correct signals were obtained from the RPNI. In other words, the nerves were activated in the correct movements.

[0152] As expected, the median RPNI signal (shown in the uppermost row of the graphs 1700 and 1702) showed similar amplitude of activity during both the thumb-little finger opposition and the thumb-only opposition. This is because the median nerve originally innervated the thumb muscles, but not the little finger muscles. The ulnar RPNI signal (shown in the lowermost row of the graphs 1700 and 1702) was more active during the thumb-little finger opposition than during the thumb-only opposition, because the ulnar nerve originally innervated more muscles of the little finger than of the thumb. Finally, the healthy FCU muscle activity, while present, was not relevant to either of the two movements, because the healthy FCU muscle activity was entirely used for flexion of the wrist.

[0153] Taken together, plots 1700 and 1702 show that the RPNI is being innervated by the intended nerve, as there should be no way to achieve the same pattern of activity via the healthy, intact muscle surrounding the RPNI.

[0154] Referring to Figure 18 , plot 1800 shows signals recorded from the ulnar RPNI during a key pinch motion, which corresponds to pinching a key between the thumb and the side of the index finger, i.e., the shape formed when turning a key in an automobile ignition switch. Plot 1800 illustrates the high signal amplitudes achievable with RPNI technology. For example, the signal-to-noise ratio (SNR) of the data in plot 1800 is 8.65.

[0155] Example 3 Continuous position control study on non-human primates In the following example, nerve signals in a monkey were sensed and monitored as the monkey flexed and extended a finger. These nerve signals were processed using the techniques described above with reference to the present teachings (including the use of a Kalman filter), and a percentage of flex was predicted based on these nerve signals. In addition, the actual percentage of flex of the monkey's finger was monitored and compared to the predicted percentage of flex.

[0156] Referring to Figure 19 , plot 1900 shows a predicted percentage of flex 1902 of a finger plotted over time, as well as an actually observed percentage of flex. In plot 1900, the predicted percentage of flex 1902 correlates with the actual percentage of flex 1904 with a correlation coefficient of 0.87. Plot 1900 illustrates the accuracy of the techniques described above with reference to the present teachings (including the use of a Kalman filter) in predicting an actual percentage of flex based on monitored nerve signals. In this way, the techniques of the present teachings can be used for continuous position control of a prosthesis. In other words, the present teachings can be used to process nerve signals, and control a prosthesis through a range of flex positions, rather than discrete prosthesis positions or prosthesis states (e.g., flex state or extension state).

[0157] Referring to Figure 20 to Figure 30 , additional embodiments of the present disclosure are shown, and these additional embodiments include systems and methods for controlling a prosthesis based on an amplified nerve signal. Referring to Figure 20, a system 200 for controlling a prosthetic device such as a prosthetic hand 110 is shown. Although the system 200 is described using the example of a prosthetic hand 110, other suitable prosthetic devices can be used. Similar to the systems and methods described above, the system 200 includes a neural interface system 4 having an implanted device 20 that receives a neural signal from a nerve 6 (e.g., a peripheral nerve) that has been amplified by a free tissue graft 10, as described above. The signal is transmitted from the free tissue graft 10 to the implanted device 20 via electrical leads 18 (e.g., the leads 18a, 18b, 18c described above with reference to Figure 1 The implanted device 20, leads 18a, 18b, 18c, free tissue graft 10, prosthetic hand 110, and neural interface system 4 for amplifying a neural signal from a nerve 6 are described above, for example, with reference to Figure 1 to Figure 7 The implanted device 20, leads 18a, 18b, 18c, free tissue graft 10, prosthetic hand 110, and neural interface system 4 for amplifying a neural signal from a nerve 6 are described above, for example, with reference to

[0158] As described in greater detail below, the system 200 is similar to the systems described above with reference to Figure 1 to Figure 7 , except that the implanted device 20 of the system 200 wirelessly transmits data regarding the amplified neural signal, e.g., processed EMG data, to a prosthetic controller 220. The prosthetic controller 220 controls an actuator 112 (as shown in Figure 1 , Figure 3 , Figure 5 to Figure 7 and Figure 9 the prosthetic hand 110 based on the received signal from the implanted device 20 in the same manner as the implanted device 20 controls the prosthetic hand 110 as described above with reference to Figure 3

[0159] ​As such, the present disclosure provides systems and methods for controlling a prosthetic hand 110 using amplified neural signals from a free tissue graft 10. The systems and methods of the present disclosure provide intuitive functional control of a multi-joint prosthetic hand for upper-limb amputee patients who have undergone a regenerative peripheral nerve interface (RPNI) surgical procedure. As described above, the RPNI surgical procedure places a separate, smaller muscle graft or free tissue graft 10 on a nerve bundle ending in the residual limb. The nerve bundle reinnervates its corresponding muscle graft to form a healthy, stable, and durable neuromuscular connection. Intramuscular bipolar electrodes, which will be further described below, can be used to record directly from the RPNI. Although the present disclosure describes example embodiments that utilize an RPNI, the systems and methods of the present disclosure can also be used for patients who have undergone a targeted muscle reinnervation (TMR) surgical procedure. The TMR procedure involves transferring residual nerves from the amputated limb to reinnervate new target muscles of the subject that were otherwise rendered functionless. The reinnervated target muscles then act as biological amplifiers of the severed nerve motor signals. Electrodes can then be attached or embedded into the target muscles and connected to the implanted device 20 of the present disclosure, which can receive the neural signals generated by the reinnervated nerves in the target muscles and process these signals in accordance with the present disclosure to control a prosthetic device. Additionally, as described below, the implanted device 20 can transmit signals to the reinnervated nerves in the target muscles to provide sensory feedback stimulation. In this way, the systems and methods of the present disclosure, including the implanted device 20, can be used for both patients who have received an RPNI and patients who have undergone a TMR procedure.

[0160] As described above, the device is a fully implantable recording system that can wirelessly transmit electromyographic (EMG) signals to an external device, such as a prosthetic controller 220, for upper-limb prosthetic control. For example, the system can include the following references Figure 20 to Figure 30Further described in detail are: (1) implantable electrodes and electrical leads 18; (2) an implantable sensing unit, e.g., implanted device 20; (3) a wireless transmitter, e.g., included in communication circuitry 50 of implanted device 20; (4) a wireless receiver, e.g., included in communication circuitry 222 of prosthesis controller 220; (5) an external intelligent link controller, e.g., prosthesis controller 220; and (6) a charging unit, e.g., external programmer charger 280 and inductive charging pad 282. As a further example, implanted device 20 can be implemented by a modified PMA-approved spinal cord stimulation device (Nuvetra Algovita, PMA P130028). However, instead of stimulation, the unit can be configured to record EMG signals from both residual muscle and RPNI using electrode leads. The sensing unit or implanted device 20 can wirelessly transmit signals to an external intelligent controller, e.g., prosthesis controller 220, which will then decode or interpret the EMG signals and send commands to an end device, e.g., a myoelectric prosthesis such as prosthetic hand 110. Additionally or alternatively, the implanted device can be configured as a stimulation device with a receiver to receive signals from a transmitter of prosthesis controller 220 and output signals to implantable electrodes / electrical leads 18, which are then amplified by neural interface system 4 to stimulate nerve 6. In this way, the system and method of the present disclosure can be used to wirelessly transmit signals from prosthesis controller 220 to implanted device 20, which are then used to stimulate nerve 6, in addition to sensing neural signals and transmitting signals from implanted device to prosthesis controller.

[0161] Reference is also made to Figure 21 Implanted device 20 includes an amplifier 24, processing circuitry 22, and communication circuitry 50 as described above. Implanted device 20 includes a battery 230 connected to amplifier 24, processing circuitry 22, and communication circuitry 50. Although Figure 21A single amplifier 24 is shown, but additional amplifiers can be used as described below. The implant device 20 receives the amplified neural signal 240 from the free tissue graft 10 via the electrical lead 18. As described in more detail below, the implant device 20 processes the amplified neural signal and wirelessly communicates data regarding the amplified neural signal, e.g., processed EMG data, to the prosthesis controller 220 via the communication circuit 50. For example, the communication circuit 50 includes a wireless transmitter configured for wireless communication. The prosthesis controller 220 includes a communication circuit 222, e.g., including a wireless receiver configured for wireless communication. Wireless communication between the communication circuit 50 of the implant device 20 and the communication circuit 222 of the prosthesis controller 220 can be performed using a Medical Implant Communication Service (MICS) protocol and / or a Medical Device Radiocommunication Service (MedRadio) protocol. Additionally or alternatively, any other suitable wireless communication protocol can be used, e.g., including Bluetooth, Bluetooth Low Energy (BLE), WiFi, near-field communication (NFC), or other suitable wireless communication protocol.

[0162] The processing circuit 224 of the prosthesis controller 220 is configured to process signals received by the communication circuit 222 from the implant device 20 and communicate a prosthesis control signal output 56 to the control actuators 112 of the prosthetic hand 110 (as shown) via a data communication bus, such as a CAN bus 226. For example, the processing circuit 224 of the prosthesis controller 220 can execute the control algorithm 900 described above with reference to FIG. 9 to control the prosthetic limb based on the received signals from the free tissue graft 10. Additionally or alternatively, as described above, the prosthesis controller 220 is also configured to process stimulation feedback signal inputs, e.g., prosthetic sensor input signals 58 generated by one or more pressure sensors 225 corresponding to pressure sensed by the pressure sensors 225 of the prosthetic hand 110. For example, when the prosthetic hand 110 grasps an object, the pressure sensors 225 detect pressure generated by the grasped object. The prosthetic sensor input signals 58 can be received via a data communication bus, such as the CAN bus 226, and processed by the processing circuit 224 of the prosthesis controller 220, which can then communicate stimulation / sensory feedback signals to the implant device 20 via wireless communication transmitted from the communication circuit 222 to the communication circuit 50. Figure 3 Figure 9 The processing circuit 224 of the prosthesis controller 220 is configured to process signals received by the communication circuit 222 from the implant device 20 and communicate a prosthesis control signal output 56 to the control actuators 112 of the prosthetic hand 110 (as shown) via a data communication bus, such as a CAN bus 226. For example, the processing circuit 224 of the prosthesis controller 220 can execute the control algorithm 900 described above with reference to FIG. 9 to control the prosthetic limb based on the received signals from the free tissue graft 10. Additionally or alternatively, as described above, the prosthesis controller 220 is also configured to process stimulation feedback signal inputs, e.g., prosthetic sensor input signals 58 generated by one or more pressure sensors 225 corresponding to pressure sensed by the pressure sensors 225 of the prosthetic hand 110. For example, when the prosthetic hand 110 grasps an object, the pressure sensors 225 detect pressure generated by the grasped object. The prosthetic sensor input signals 58 can be received via a data communication bus, such as the CAN bus 226, and processed by the processing circuit 224 of the prosthesis controller 220, which can then communicate stimulation / sensory feedback signals to the implant device 20 via wireless communication transmitted from the communication circuit 222 to the communication circuit 50.

[0163] Reference​Figure 22 shows four electrical leads 18, where each electrical lead 18 includes two wires connected to the positive electrode 252 and the negative electrode 254, respectively. Figure 23 shows a photograph of the electrical leads 18, the positive surface of the positive electrode 252, and the negative surface of the negative electrode 254. In the example of Figure 22 , the eight wires of the electrical leads 18 are connected to the eight- contact connector 250. Specifically, as shown in Figure 22 , the eight contact wires of the eight-contact connector 250 are connected to the exposed wires of the electrical leads 18, for example, with platinum crimp connectors, which are then sealed with silicone tubing. Although Figure 22 the example in uses platinum crimp connectors with silicone tubing to connect the contact wires of the eight-contact connector 250 with the exposed wires of the electrical leads 18, any other suitable method for connecting the contact wires of the eight-contact connector 250 with the exposed wires of the electrical leads 18 can be used. Alternatively, the exposed wires of the electrical leads 18 can be directly connected to the contacts of the eight-contact connector 250. Additionally, although Figure 22 the example in uses four dual-wire electrical leads 18 and an eight-contact connector 250, any other suitable number of wires, electrical leads, and contact connectors can be used. For example, a contact connector can include contacts for receiving two, four, six, ten, twelve, or any other suitable number of electrical wires, and an electrical lead can include two, four, six, ten, twelve, or any other suitable number of electrical wires.

[0164] To record EMG signals from residual muscle and RPNI, the system 200 utilizes an electrode and lead assembly that connects the muscle tissue (e.g., the free tissue graft 10) to the implanted device 20. This element can include a modified version of an existing bipolar electrode, as well as a modified version of an existing cable lead that connects to the implanted device 20. For example, each implanted device 20 can be connected to twelve bipolar electrodes, with each lead containing eight contacts, and three leads per implanted device 20. The bipolar electrode can be modified from the PermaLoc™ electrode, which has been approved by the U.S. Food and Drug Administration (FDA) as part of the long-term implanted system NeuRx Mempatch pacing system. As shown in Figure 23 , the bipolar electrode has an additional de-insulated lead surface compared to the monopolar PermaLoc™ electrode, and the plastic tinned anchor at the distal end of the electrode is removed. The basic design, all material components, and sterilization procedures remain the same. The electrode can be implanted percutaneously in a patient, for example, an upper extremity amputee, to record EMG signals from residual muscle and RPNI.

[0165] Several modifications and additions were made for the case where the bipolar electrode is fully implanted with and connected to the implant device 20. To connect to the implant device 20, an industry standard Bal Seal connector lead was used, manufactured by Cirtec Biomedical, Inc. The single Bal Seal connector lead has eight contacts, with eight conductors parallel to the lead body. Other numbers of contacts can be used, such as twelve conductors. The lead diameter is 0.053 mm, with an estimated impedance of 4 ohms per foot. The conductor material used is MP35N with 28% silver, and the outer insulation is 55D Pellethane, with conductor insulation of ETFE. The electrical connection to the Bal Seal uses a platinum-iridium connector ring. The exposed cable is exposed at 10 cm to 15 cm from the proximal end of the Bal Seal connector lead, and exposed at the proximal end of the PermaLoc bipolar lead. The distal and proximal ends of the two leads are permanently connected to form a single lead with 4 bipolar electrodes. The platinum crimp connectors are used to connect each individual exposed wire, and silicone tubing will be used to cover these platinum crimp connectors for protection. The protected junctions are located as close to the implant device 20 as possible, to avoid anatomical joints in the patient from crossing the thicker portions of the lead. The total length of the assembled lead is approximately 100 cm to 105 cm from the proximal end to the distal end. Thus, the distal and proximal ends of two previously approved electrode leads are permanently paired, without changing the electrical or material properties of these leads.

[0166] Reference is made to Figure 24A , the implant device 20 includes a header that accepts one or more contact connectors that are connected to electrical leads. For example, as shown in Figure 24A , the implant device includes electrical ports 256, each configured to accept an eight-contact connector 250. Each port 256 can include a Bal Seal connector, such as a Bal Seal connector manufactured by Cirtec Biomedical, configured to seal the implant device 20 once the eight-contact connector 250 is inserted into the electrical port. The implant device also includes a header with parallel conductors 254 having contacts positioned and spaced to correspond to and align with the contacts of the eight-contact connector 250. In this example, the Bal Seal conductors 254 are manufactured by Bal Seal Inc. and are components of the header manufactured by Cirtec. In the example of Figure 24A , the implant device is configured with a header having three parallel conductors 254 for accepting three eight-contact connectors 250. In this way, Figure 24AThe illustrated implant device 20 can accommodate signals from up to 24 leads, i.e., signals from three sets of eight electrical leads based on signals from 24 electrodes, including twelve positive electrodes 252 and twelve negative electrodes 254. The twelve positive electrodes 252 and twelve negative electrodes 254 enable the implant device 20 to receive EMG activity for twelve bipolar channels based on amplified neural signals 240 from the free tissue graft 10. Although Figure 24A The example of FIG. 2 utilizes 24 leads and 24 electrodes, but the implant device 20 can be configured with a header and parallel conductors 254 to accommodate any number of leads and electrodes. For example, the implant device 20 can include a header with two sets of parallel conductors 254 each including 12 contacts to accept a twelve-contact connector. Alternatively, the implant device 20 can include four sets of parallel conductors 254 to accept four eight-contact connectors 250, so that signals from four sets of electrical leads based on signals from 32 electrodes, including 16 positive electrodes 252 and 16 negative electrodes 254, can be received. In this manner, the implant device 20 can include a header with any suitable number of parallel conductors 254 having any number of contacts to accept a contact connector having any number of leads.

[0167] Implant device 20 can be implemented using a modified implantable recording device based on the Algovita platform developed by Cirtec Biomedical. The Algovita platform (PMA P130028) is an FDA-approved implantable pulse generator for reducing pain via spinal cord stimulation. The electrode lead connects to implant device 20 with a header having 3 rows, each row containing 8 spring platinum iridium Bal Seals. The lead is secured after implantation with titanium set screws that are tightened with a torque wrench that inserts a septum. The Algovita is internally powered by a 4.1 V lithium-ion battery with a nominal capacity of 215 milliampere hours (mAh). The lithium-ion battery has been tested to retain capacity after 1000 discharge cycles. The lithium-ion battery also has a low self-discharge rate, so shelf life is not an issue for this application as the battery exceeds the shelf life of the sterile packaging. Implant device 20 wirelessly communicates with external components through a medical implant communication system (MICS) with a communication circuit 50, which can include, for example, a Microsemi ZL70323MNJ, a newer version of the MICS chip used in the original Algovita. MICS communication uses the 2.45 gigahertz (GHz) band for wake-up and the 402 megahertz (MHz) to 405 MHz band for data transmission. The battery, power management, and communication circuitry remain unchanged except for the updated MICS chip.

[0168] The external physical design of implant device 20 remains the same as the Algovita device. The internal component changes to convert the Algovita to a sensing device are limited. The Saturn stimulator application-specific integrated circuit (ASIC) is replaced with an Intan RHD2216 amplifier for sensing bioelectric potentials instead of stimulating neural tissue. The Intan amplifier has low power consumption and has been previously used in surgically invasive clinical studies. No adverse reactions were found in these acute studies. As an example, a linear voltage regulator (Texas Instruments TPS7A2033) can take the 4.1 V battery voltage as input and convert it to a 3.3 V signal to power the RHD2216. The MICS module on the Algovita (MicroSemi ZL70321MNJ) is replaced with a newer version from MicroSemi, ZL70323MNJ.

[0169] References Figure 24BAnother embodiment is shown with an alternative arrangement of six electrical leads 18' comprising six bipolar electrodes connected to a twelve-contact connector 250'. Similar to the above references... Figure 22 to Figure 24A In the described configuration, the twelve-contact connector 250' can be received by the head of the implantable device 20. As described above, the port 256 of the implantable device 20 may include a Bal Seal connector, such as a Bal Seal connector manufactured by Cirtec Biomedical, which is configured to seal the implantable device 20 once the twelve-contact connector 250' is inserted into the electrical port of the implantable device 20. The contacts of the twelve-contact connector 250' are sufficiently spaced to match the spacing of the corresponding connector on the head of the implantable device 20. Additionally, in the embodiment of FIG. 24, a triangular coupling device 251 is used to receive six electrical leads 18' and couple these six electrical leads 18' to the twelve-contact connector 250'. Figure 24B As shown, the coupling device 251 includes a parallel connection of six electrical leads 18' on the wider portion of the delta coupling device 251, and couples twelve wires of six bipolar electrodes to a single cable, wherein each of these twelve wires is connected to a single contact of a twelve-contact connector 250'.

[0170] refer to Figure 25 The implantable device 20 includes protection circuitry to protect its electronic components from surge voltages during electrostatic discharge (ESD) events. For example, the implantable device 20 may experience an ESD event when manipulated during a surgical procedure to implant it into a patient. Figure 25 As shown, a 220-ohm resistor 262 is located before the input of amplifier 24 to provide protection against surge voltages that may build up in the event of an ESD event. As an example, the 220-ohm resistor 262 could be an AC0603JR-07220RL resistor available from Yageo. As a further example, such as... Figure 25 As shown, amplifier 24 can be an Intan RHD2216 amplifier for sensing bioelectric potential. Additionally, as a further example, ESD diode 260 can be located on the side of resistor 262 opposite to the input of amplifier 24. For example, diode 260 can be an SMS24T1G ESD diode available from On Semiconductor. Although... Figure 25The diagram shows an ESD protection circuit for one input of the implantable device 20, but for each input of the implantable device 20, the same protection circuit can be included, comprising a 220-ohm resistor 262 and an ESD diode 260. In this way, as... Figure 21 to Figure 2 As shown in Figure 4, for an implantation device 20 configured with a head for three sets of contactors 254 for three sets of eight-contact connectors 250 (i.e., a total of 24 inputs), the implantation device 20 may include 24 sets of contactors 254. Figure 25 The protection circuit shown. In other words, for each of the two inputs of the implantable device 20, the implantable device 20 may include a 220-ohm resistor 262 and an ESD diode 260. Although Figure 25 The diagram shows a specific example of resistor 262 and diode 260, but any other suitable resistor and diode can be used to protect the implanted device 20 from ESD events.

[0171] refer to Figure 26 A functional block diagram of a system and method for processing amplified neural signals according to this disclosure is shown. Figure 26 As shown, the implanted device 20 receives the raw amplified neural signal 240. For example, as Figure 21 to Figure 2 As shown in Figure 4 and as described above, the implantable device 20 receives EMG activity from twelve bipolar channels of the implanted electrodes and leads 18. At 270, the amplified neural signal is filtered using a bandpass filter ranging from 100 Hz to 500 Hz, which has been shown to provide accurate measurements of EMG activity. At 272, the filtered signal is then sampled by the processing circuitry 22 of the implantable device 20 at a Nyquist rate of approximately 1 kHz to generate approximately 1,000 samples per second (1 KSps) of EMG data. At 274, the processing circuitry 22 of the implantable device 20 then calculates the mean-absolute-value (MAV) of the activity for each bipolar channel. At 276, the processing circuitry 22 uses the communication circuitry 50 of the implantable device 20 to wirelessly transmit the MAV of the activity for each bipolar channel to the communication circuitry 222 of the prosthesis controller 220 using the MICS protocol.

[0172] As discussed above, to wirelessly transmit data to and receive data from non-implanted devices such as the prosthesis controller 220 or another external communication device, the implanted device 20 can use the communication circuit 50 from the existing MicroSemi MICS communication device from the Algovita platform. After implantation, the implanted device 20 can be turned on / off by waving a magnet over the implanted device 20. While the implanted device 20 is running, it can interact with two external devices: the programmer charger discussed below; and the prosthesis controller 220, also known as the Smart Link Controller (SLC). As discussed above, the implanted device 20 is configured to wirelessly stream EMG to the prosthesis controller 220. The implanted device 20 includes a bootloader for wireless firmware updates. The microcontroller of the implanted device 20 is a Texas Instruments MSP430F2618 (MSP430) also used for the Algovita platform. The microcontroller is programmed to record EMG activity from 12 bipolar channels from implanted electrodes using Intan RHD2216 amplifiers.

[0173] In this manner, rather than transmitting raw EMG data to the prosthesis controller 220, the implanted device 20 first filters, samples, and processes the raw EMG signals from the bipolar channels to first calculate the MAV of the sampled EMG signals for each bipolar channel, and then only wirelessly transmits the processed MAV data for each bipolar channel to the prosthesis controller 220. This configuration provides the technical advantage of maximizing the battery life of the battery 230 of the implanted device 20 and the battery life of the power source of the prosthesis itself, as transmitting the higher bandwidth raw EMG signals directly from the implanted device 20 to the prosthesis controller 220 would require more power. In this manner, the system and method of the present disclosure maximizes the battery life of the device by first filtering, sampling, and processing the raw EMG data at the implanted device 20, and then only wirelessly transmitting the processed EMG signals for each bipolar channel to the prosthesis controller 220.

[0174] Reference is made to Figure 27 FIG. 1 1 shows an example of the prosthesis controller 220 both externally to the prosthetic hand 1 10 and mounted internally within the prosthetic hand 1 10. Figure 27 A quarter is also shown in FIG. 1 1 to illustrate the relative size and scale of the prosthesis controller 220. As shown in FIG. 1 1, the prosthesis controller 220 can be implemented using a printed circuit board assembly (PCBA). Figure 27

[0175] ​The prosthetic controller 220 receives the processed EMG data packets and decodes these EMG data packets into motion commands for the prosthesis. For example, the prosthetic controller 220 includes a communication circuit 222, which can include, for example, a ZL70123 chip, to communicate wirelessly and securely with the implanted device 20 using the standard MICS protocol. All components of the prosthetic controller 220 are soldered onto a PCBA, and the device is enclosed in a waterproof housing within the prosthesis. The materials used in the prosthetic controller 220 are typical for modern PCBAs. The circuit board is a flat laminated composite material with internal copper circuits. The components mounted on the circuit board are application specific integrated circuits (ASICS) intended for use by the manufacturer. The ASICS are attached to the circuit board using industry standard surface mount technology, using lead-free solder.

[0176] As described above, the prosthetic controller uses state-of-the-art algorithms, including machine learning algorithms, to decode the EMG signals into motion commands. The choice of algorithm depends on the mode of operation of the prosthetic hand. A classifier can be used to decode for a grasp for intuitive grasp selection, with additional gain for proportional control. Alternatively, a regression can be used to control individual fingers simultaneously and independently.

[0177] The prosthetic controller can be connected to a laptop computer (e.g., the external programmer device 290 described below) to record the processed EMG from the implanted device 20. As described below, the implanted device 20 can also be wirelessly connected to and communicate with a laptop computer (e.g., the external programmer device 290). The recorded EMG can be used to measure the signal strength from each electrode pair and to calculate decoding parameters. A configuration mode can be used to load new parameters onto the prosthetic controller 220. A test mode can be used to verify the prosthetic controller functionality of the implanted device 20. In the test mode, the external programmer device 290 can transmit pre-recorded EMG data packets to the prosthetic controller, which will respond with the decoded motion commands. In other aspects, the device operation is fixed and includes: receiving a signal; decoding an intended motion; and sending an output to the prosthesis.

[0178] The power for the prosthetic controller 220 is provided by the battery of the prosthesis. The prosthetic controller 220, once activated, automatically connects to the prosthetic hand 110 and waits to receive signals from the implanted device 20. When the prosthetic controller 220 does not receive a signal from the implanted device 20, the prosthetic controller will stop sending predictions of motion to the prosthetic hand 110. This will prevent unintended or unexpected motion of the prosthesis.

[0179] References Figure 28An external programmer charger 280 and an inductive charging pad 282 for charging the implantable device 20 are shown. To charge the implantable device 20 after it has been surgically implanted into the patient, the inductive charging pad 282 can be aligned with the patient near the implantable device 20 inside the patient's body. For example, if the implantable device 20 is as follows... Figure 20 As shown, when implanted into a patient's chest, the inductive charging pad 282 can be placed on the patient's chest, approximately above the location of the implanted device 20 within the patient's chest. The external programmer charger 280 and / or the inductive charging pad 282 can generate an output, such as illuminating a light-emitting diode (LED) or generating an audible sound, to indicate that the inductive charging pad 282 is aligned with the implanted device 20 for inductive charging. Once aligned and communicating, the external programmer charger 280 and the inductive charging pad 282 can charge the battery 230 of the implanted device 20 while it is being implanted into the patient's body. The same patient programmer charger (PPC) from the Algovita platform can be used to wirelessly charge the internal battery of the ISU using 40 kHz, 0.65 watts (W) inductive coupling. The PPC can also be used to check the battery level of the ISU.

[0180] refer to Figure 29communicates with the communication circuit 222 of the prosthesis controller 220. When the implant device 20 is implanted within the patient's body, communication between the external programmer device 290 and the communication circuit 50 of the implant device 20 can be performed wirelessly. Communication between the external programmer device 290 and the communication circuit 222 of the prosthesis controller 220 can be performed wirelessly or via a wired communication connection. The external programmer device 290 can be implemented with a computing device, such as a laptop, desktop, tablet device, mobile device, or any other suitable computing device configured with an application configured to communicate with the implant device 20 and the prosthesis controller 220. For example, as described above, the external programmer device 290 can receive the EMG signals from the amplified neural signals 240 of the implant device 20, including the raw EMG signals or the filtered, processed, and sampled EMG signals. As a further example, the external programmer device 290 can use the filtered, processed, and sampled EMG signals from the implant device 20 to determine configuration parameters for the prosthesis controller 220. Additionally or alternatively, after the filtered, processed, and sampled EMG signals are received from the implant device 20, the external programmer device 290 can receive the filtered, processed, and sampled EMG signals from the communication circuit 222 of the prosthesis controller 220. The external programmer device 290 can then determine configuration parameters to be used by the prosthesis controller 220 to control the prosthetic hand 110 based on the filtered, processed, and sampled EMG signals from the implant device 20. The external programmer device 290 and / or the prosthesis controller 220 can also use machine learning algorithms to determine and adjust configuration parameters to be used by the prosthesis controller 220 to control the prosthetic hand 110 based on the filtered, processed, and sampled EMG signals from the implant device 20.

[0181] Reference is made to Figure 30 , showing another embodiment of the present disclosure. Figure 30 The system 300 in FIG. 3 is similar to the system 200 described above, except that the system 300 does not include the implant device 20 implanted within the patient. Rather, Figure 30 The system 300 in FIG. 3 is configured for testing and configuration prior to the implant device 20 being implanted within the patient. Similar to the system 200 described above, the system 300 includes the neural interface system 4 having the neural signals from the nerve 6 (e.g., a peripheral nerve) that have been amplified by the free tissue graft 10, as described above. However, in Figure 30In system 300, electrical leads 18 extend through port 302 to the outside of the patient's body and connect to interface 304. Interface 304 connects to an external programmer device 290, which communicates with the prosthetic controller 220 of the prosthetic hand 110. System 300 can be used to test and calibrate the parameters of the prosthetic controller 220 before the implantable device 20 is implanted into the patient's body, and with or without the prosthetic hand 110 attached to the patient. In this way, system 300 can be used to examine and analyze neural signals generated by nerve 6 and amplified by the free tissue graft 10 to determine whether these signals are sufficient for the prosthetic controller 220 to control the prosthetic hand 110 before the implantable device is implanted into the patient's body.

[0182] As described above, the implantable device 20 of this disclosure can be used to generate prosthetic control signal output 56 for controlling a prosthetic device (e.g., prosthetic hand 110), and also to receive stimulation feedback signals, such as prosthetic sensor input signals 58 generated by one or more pressure sensors 225, which are used to provide stimulation to the nerve 6 and sensor feedback signals. For example, the implantable device 20 may include one or more Intan RHS2116 stimulator / amplifier chips configured to stimulate nerve tissue and sense biopotential. The RHS2116 stimulator / amplifier chip provides current-controlled stimulation pulses of up to 2.55 milliamperes (mA) to individual electrode contacts, which have been shown to be sufficient to elicit sensing by the sensor. The stimulation pulses can be delivered in a biphasic manner to achieve charge balance. The RHS2116 stimulator / amplifier chip has a fast amplifier reset function to eliminate stimulation artifacts before sensing biopotential. The RHS2116 stimulator / amplifier chip can be powered by a 3.3-volt linear voltage regulator (such as Texas Instruments' TPS7A2033). Alternatively, a bipolar output power supply can be used to provide a positive or negative voltage supply of up to ±7 volts (+ / -7 V) for stimulating the RHS2116 stimulator / amplifier chip.

[0183] In one embodiment, the systems and methods of this disclosure can be used for both: (1) receiving signals from a free tissue graft to generate a prosthesis control signal output 56 for controlling a prosthetic device (e.g., prosthetic hand 110); and (2) receiving stimulation and sensory feedback signals, such as prosthetic sensor input signals 58 generated by one or more pressure sensors 225 of the prosthetic device (e.g., prosthetic hand 110). However, one problem with simultaneously performing motion control and sensory / stimulation functions is that stimulation signals may introduce artifacts or noise into the signals sensed and recorded for motion control of the prosthetic device. Therefore, the implantation device 20 of this disclosure is configured to use the following references Figure 31 to Figure 33One or more of the methods and algorithms described are used to mitigate and / or eliminate signal distortion and artifact problems. Figure 31 to Figure 33 Different methods and algorithms are shown for mitigating and / or eliminating signal distortion and artifacts in sensed and recorded signals for motion control of prosthetic devices.

[0184] For example, refer to Figure 31 This illustrates an algorithm for generating prosthetic motion commands and executing stimuli during alternating time periods. Figure 31 to Figure 33 In the diagram, time periods t-3, t-2, t-1, and t are shown from left to right along the horizontal axis. These time periods can be, for example, from 10 milliseconds to 50 milliseconds, but any suitable time period length can be used. Furthermore, X represents the prosthesis movement command generated by the implanted device 20 during the time period indicated by the subscript. Additionally, Y represents the EMG signal received from electrodes implanted or attached to the RPNI, residual muscle, and / or target muscle during the time period indicated by the subscript after the TMR procedure. In time period t-3, the stimulation function is initially turned off, and the implanted device 20 bases its signal on the EMG signal Y. t-3 Generate prosthetic motion command X t-3 In time period t-2, a stimulus event is detected that continues throughout time periods t-2, t-1, and 5 in the example. For example, this stimulus event could be receiving a signal from the pressure sensor 225 of the prosthetic device. In other words, the stimulus can be initiated and used once a need to provide stimulation for sensor perception is detected (e.g., when the pressure sensor 225 detects an increase in pressure on the prosthetic device and generates a pressure signal).

[0185] exist Figure 31 In the illustrated method and algorithm, the implanted device 20 alternates between executing sensory stimuli and generating prosthetic movement commands during consecutive time periods. For example, sensory stimuli are executed when the stimulation function is on during time periods t-2 and t; and when the stimulation function is off during time periods t-3 and t-1, the stimulation function is based on EMG signal Y. t-3 and Y t-1 Generate prosthetic motion command X t-2 and X t-1 In this manner, prosthetic movement commands are generated during time periods t-3 and t-1, and perceptual stimuli are administered during time periods t-2 and t. In this manner, the generation of prosthetic movement commands is temporarily suspended during time periods t-2 and t, while stimuli are administered. Similarly, stimuli are temporarily suspended during time periods t-3 and t-1, while prosthetic movement commands are generated. Figure 31In the example, after applying the stimulus and subsequently moving to the next time interval (e.g., t-1) used to generate the prosthesis movement command, the position of the prosthesis and / or the state of the prosthesis command are in the same position or state as at the end of the previous time interval (e.g., t-3) used to generate the prosthesis movement command. In other words, in Figure 31 In the method and algorithm, the implanted device 20 alternates between time windows of: (A) generating a prosthesis movement command X when the stimulus is off; and (B) pausing prosthesis control when the stimulus is on. In this way, the implanted device 20 switches back and forth between receiving stimuli and sending control signals in each time period (e.g., every 10 milliseconds to 50 milliseconds).

[0186] exist Figure 31 In the method and algorithm, the prosthetic movement command is not updated during the stimulus time interval (e.g., t-2 and t). In other words, if the prosthetic movement command at the end of time interval t-3 is command prosthesis 110 according to command X... t-3 If the indicated speed is closed, then at the beginning of time period t-1, the dummy hand will still operate according to the previous command X. t-3 The same velocity indicated is used for closure. This method and algorithm can be implemented using multiple control algorithms, including: estimating regressors of the kinematics, such as linear regression or neural networks; or estimating discrete state classifiers, such as linear discriminant analysis, Naive Bayes, support vector machines, and / or neural networks.

[0187] refer to Figure 32 This demonstrates another algorithm for generating prosthetic motion commands and executing stimuli. Figure 32 The methods and algorithms shown are similar to those in the reference. Figure 31 The methods and algorithms shown are only Figure 32 The methods and algorithms shown estimate prosthetic motion commands during the time period of stimulus execution. For example, in time periods t-2 and t, the implanted device 20 estimates the prosthetic motion commands (e.g., X) based on previously generated prosthetic motion commands. t-3 and X t-1 To generate the estimated prosthetic motion command X t-2 and X t .For example, Figure 32 The methods and algorithms in [the text] are not simply (as described by [the text]). Figure 31the method and algorithm in Figure 31 , the method in Figure 32 alternates between sensing and receiving signals from the electrodes (e.g., in time periods t-3 and t-1) and sending stimulation signals for perceptual awareness (e.g., in time periods 5-2 and 5). In addition, the implanted device 20 does not read or sense any signals from the electrodes during the time periods when stimulation is performed, e.g., during time periods t-2 and 5. However, the implanted device 20 can generate estimated prosthesis movement commands (e.g., X t-2 and X t ) to provide a smoother series of commands over time, rather than pausing generation of prosthesis movement commands during time periods when stimulation is performed. In this way, Figure 32 the method and algorithm in t-3 and X t-1 ) and avoid jumps between commands that can occur in the method and algorithm in Figure 31 . In this way, Figure 32 the method and algorithm in

[0188] Referring to Figure 33 , another algorithm for generating prosthesis movement commands and performing stimulation is shown. In the method and algorithm of Figure 33 , prosthesis movement commands are generated in all time periods. However, during time periods when stimulation is also performed (e.g., t-2 and t), the implanted device 20 performs artifact estimation to estimate artifacts introduced into the EMG signals Y from the electrodes due to stimulation, then subtracts or filters out the estimated artifacts from the EMG signals Y, and generates prosthesis movement commands X based on the resulting / fiitered EMG signals after artifact subtraction. In this way, the implanted device 20 can remove artifacts that can be introduced into the EMG signals due to performing stimulation while reading the neural signals from the electrodes attached to the free tissue graft 10. In other words, the implanted device performs noise reduction, filtering, or cancellation on the EMG signals Y to remove artifacts that can have been introduced into the signals by performing stimulation for perceptual awareness.

[0189] For example, a template subtraction algorithm can be used to generate an expected artifact based on a given stimulation signal or a set of stimulation signals. The template can be used to subtract the artifact from the EMG signal. In other words, under this approach, the EMG signal Y can be corrupted by the stimulation-induced perceptual artifact, but the algorithm or approach filters the EMG signal Y to subtract the artifact so that a prosthesis motion command can be generated based on the filtered EMG signal Y. For example, the template subtraction algorithm can be implemented by using multiple exponential filters with a filter learning rate to compute an average of the artifact immediately following a stimulation pulse. In this way, a representative template can be constructed and then subtracted from the original signal to produce an estimated artifact-free signal.

[0190] Additionally or alternatively, an epsilon normalized least mean square algorithm can be used to remove unwanted artifacts from the EMG signal Y. For example, the epsilon normalized least mean square algorithm can be implemented as a modified version of the standard least mean square algorithm that produces better performance for signals with intervals of larger and lower signal energy. In the epsilon normalized least mean square algorithm, the self-adjusting filter relies on a reference signal that is highly correlated with the stimulation artifact. The algorithm can adapt to different artifact waveforms without requiring a complete relearning of the weights used by the algorithm. For example, the epsilon normalized least mean square self-adjusting filter algorithm convolves the reference signal with the filter weights to predict the artifact waveform. The predicted artifact is subtracted from the original signal to produce an estimated artifact-free signal, which is then also used to update the filter weights after each sample.

[0191] Although Figure 33 The examples in Figure 33 The stimulation can be performed in alternating time periods, as shown in the examples in

[0192] Although any of the individual algorithms or approaches in Figure 31 to Figure 33 However, the implanted device 20 can alternatively be configured to perform all three approaches and / or switch between the approaches to determine and select the best approach or algorithm for a particular environment or scenario.

[0193] Although Figure 31 to Figure 33The method of performing stimulation of sensory perception is shown in alternating time periods, but another order can alternatively be used. For example, stimulation can be performed in two or more consecutive time periods, followed by reading of the EMG signal Y in one or more time periods to generate the prosthesis motion command.

[0194] Multiple sensing electrodes Reference Figure 34 Another embodiment of the present disclosure is shown. In particular, a neural interface system 4a is shown that is similar to the neural interface system discussed above with reference to Figure 1 However, the embodiment in Figure 34 The embodiment in includes multiple sensing electrodes 14a, 14b, 14c on a single free tissue graft 10. The sensing electrodes 14a, 14b, 14c sense and receive neural signals amplified by the free tissue graft 10 at different locations on the free tissue graft 10. The sensed and received signals are transmitted from the sensing electrodes 14a, 14b, 14c to the implanted device 20 via leads 18d, 18e, 18f. The leads 18d, 18e, 18f can be configured in a single lead bundle with respective lead ends connected to the electrodes 14a, 14b, 14c and the implanted device 20. The sensed and received signals can be amplified by amplifiers 24a, 24b, 24c and transmitted to the processing circuit 22. In this way, the multiple electrodes 14a, 14b, 14c can be located at multiple different points on a single free tissue graft 10 of an RPNI. As discussed above, one or more portions of the nerve 6 (nerve fascicles 8) can regenerate within the free tissue graft 10 to reinnervate the tissue. The reinnervation can include growing out nerve fibers 12 that can reinnervate the tissue throughout the muscle tissue. The multiple electrodes 14a, 14b, 14c can be used at different locations on the free tissue graft 10 to sense and receive different motor control signals from the RPNI. Different signals can be received by the implanted device 20 as different motor control inputs and used for different purposes, such as to control different portions of a prosthesis. For example, multiple electrodes 14a, 14b, 14c located at different locations on a single free tissue graft 10 of an RPNI can be used to receive different motor control signals that in turn are used to control different fingers of a prosthetic hand 110. Although a prosthetic hand 110 is provided as an example, any other type of prosthetic device can be used with the systems and methods of the present disclosure.

[0195] In this way, a multi-electrode array (e.g., electrodes 14a, 14b, 14c) can sense independent motion signals from a single RPNI structure to achieve highly accurate prosthesis control. When multiple functional nerves innervate the free tissue graft 10, functionally distinct motor end plates are innervated throughout the entire region of the free tissue graft 10. (See above reference...) Figure 1 The method discussed involves using a single electrode on each free tissue graft 10 to sense and record the sum of nearby motor activity. This approach may be sufficient when only a single or limited function of the nerve is required. However, compared to… Figure 34 Compared to a neural interface system 4a that includes multiple sensing electrodes 14a, 14b, 14c on a single free tissue graft 10, using a single electrode may provide less complete and lower-resolution readings of signals from nerves and nerve function. This embodiment may include arrays of electrodes 14a, 14b, 14c with a single lead bundle (i.e., bundled leads 18d, 18e, 18f) and multiple electrode contacts (e.g., a high-density electrode device including numerous contacts), or a system in which multiple leads and electrodes are independently implanted into the RPNI. This device allows independent signals to be recorded from a single RPNI free tissue graft 10. For example, in a radial amputee, the severed nerve is responsible for the function of multiple fingers and the thumb. Even if the nerve can be divided to generate several RPNIs, these RPNIs will likely each contain multiple functions. The multi-channel electrode of this embodiment can record independent signals from the RPNIs and significantly improve the accuracy of prosthesis control.

[0196] Multiple sensing electrodes for regenerative peripheral nerve interface (RPNI) The basic functional unit of skeletal muscle is the motor unit, which refers to a motor neuron and all the muscle fibers it innervates. A single muscle contains hundreds of motor units, each with a different number of fibers that produce varying degrees of contractile force. In the RPNI (Reproductive-Related Muscle Injection), a motor unit is formed when a motor neuron in a transected nerve re-innervates existing nerve fibers in a muscle tissue graft. Figure 35 As shown, histological imaging 350 reveals different motor units forming a neuromuscular junction within a single RPNI.

[0197] A single RPNI can be created on a branch of a larger nerve, or alternatively, a larger nerve can be split into several bundle groups, with a different RPNI structure for each bundle group. The hand and fingers are controlled by thirty-four muscles. Thumb movement is itself controlled by nine muscles, half of which are located within the hand. If the median nerve is split to form two to three RPNIs, for example, each RPNI will have a reinnervated area from a bundle with multiple functions. In other words, a median RPNI can be reinnervated by a bundle that controls thumb opposition as well as a bundle that controls index finger flexion. Thus, motor units in the RPNI can be independently activated to produce multiple different movements corresponding to the somatotopic topology of the nerve. When creating RPNIs on nerves that control multiple hand functions, the motor unit topology can be exploited to reinnervate the muscle tissue, which produces different contraction areas for different movements. Figure 36 RPNIs on the median nerve are shown to contract different areas in response to different individual finger movements and thumb movements when viewed under ultrasound. Similarly, Figure 37 RPNIs on the ulnar nerve are shown to contract different areas in response to different individual finger movements and thumb movements when viewed under ultrasound. In Figure 36 and Figure 37 the arrows show which areas of the RPNI map to which joints of the hand fingers in the ultrasound image.

[0198] Within the RPNI, individual motor unit activity can be recorded by a single implanted electrode. In the example shown in Figure 38A and Figure 38B different motor units activate different movements, such as small finger flexion and finger adduction controlled by the ulnar nerve. Existing control strategies rely on software to resolve these two movements differently from a single RPNI.

[0199] Electromyography from a single electrode is processed into features and algorithms interpret differences in the processed signals, or individual motor units can be inferred via signal decomposition. However, as the complexity and number of movements increase relative to the number of RPNIs, these methods cannot reliably scale. Furthermore, signal amplitude decays exponentially with distance from the surface where the electrode is recorded. Simulations have estimated that the majority of motor unit activity recorded from intramuscular electrodes can come from muscle fibers within 1.5 mm to 2 mm of the electrode, depending on size and contact spacing. Motor units further from the electrode are recorded with lower signal-to-noise ratio and are not as reliably captured as motor units near the electrode.

[0200] With the systems and methods of the present disclosure, multiple sensing electrodes can capture activity from different motor units on independent hardware channels. For example, Figure 39Two motor units 1 and 2 are shown, which are connected to two channels 1 and 2, respectively, which are input to processing circuitry 22 for processing. This enables control of a larger number of movements with higher sensitivity. Importantly, this is a more reliable approach to increasing signal resolution than software decomposition. The recorded signals from individual contacts of multiple sensing electrodes will be dominated by the nearest motor unit. Continuing the above example of two movements, motor unit 1 can be directly mapped to movement 1, and motor unit 2 can be directly mapped to movement 2. For example, processing circuitry 22 can utilize input received via channel 1 to generate control signal 391 corresponding to movement 1, and can utilize input received via channel 2 to generate control signal 392 corresponding to movement 2. This mapping can be performed manually, or an algorithm can be used to understand the relationship for convenience. The increase in signal independence guarantees that this approach will perform more reliably than previous approaches using a single channel and software decomposition.

[0201] Implanting multiple sensing electrodes can also increase the sampling area to fully cover the RPNI. Due to these two factors, multiple sensing electrodes can more fully utilize the inherent information in a single RPNI structure for prosthesis control.

[0202] In one embodiment, rather than implanting a single electrode into the RPNI, multiple instances of the same electrode can be implanted into opposite regions or lengths of the RPNI. For example, Figure 34 Sensing electrodes 14a, 14b, 14c are shown along the length of a single free tissue graft 10. These electrodes can have a similar geometry to the intramuscular EMG electrodes currently in use. Each electrode can have a single monopolar recording contact or two bipolar (differential) contacts, and is typically implanted along the length of the RPNI. In one example, to optimize placement of multiple electrodes in the RPNI, each electrode can have a diameter of less than 1.5 mm, a recording contact length of 2 mm to 1 cm, and a bipolar contact-to-contact spacing of less than 2 cm. However, these dimensions and placements are provided as examples only, and other dimensions and placements can also be used. Implanting two or more of these electrodes in this manner can be sufficient to span a larger RPNI and record independent signals from different functional regions.

[0203] In another embodiment, as Figure 40As shown, the recording resolution can be further increased with a greater number of smaller recording contacts 400 for each electrode. For example, a high resolution electrode can have two or more smaller (e.g., less than 2 mm) contacts. An example electrode of this type can have eight contacts, each of which is 0.2 mm or less. These dimensions are provided by way of example only, however, and other dimensions can be used. Each contact of an electrode selectively records a few distinguishable and independent motor units. As Figure 40 As shown, a high resolution electrode can be implanted across the width of an RPNI to pass through multiple muscle fibers along the length of the RPNI. Alternatively, a matrix array of contacts across multiple dimensions of the RPNI can be used. A single or multiple high resolution electrodes can be inserted into an RPNI.

[0204] Referring to Figure 41 , the processing circuit 20 can include multiple independent input channels for each RPNI. For example, the RPNI 410 generates an output of eight channels that are input to the processing circuit 20. The RPNI 412 generates an output of two channels that are input to the processing circuit. The RPMI 414 generates an output of one channel that is input to the processing circuit 20. In Figure 41 an example, the processing circuit can be included in a single implanted device 20 with amplifiers and circuitry as described above to receive input from the individual channels of the RPNI 410, 412, 414.

[0205] Referring to Figure 42 , in another embodiment, if multiple high resolution electrodes are used, a network of implanted devices with multiple modules can be used. For example, a wireless body area network (BAN) can be used to communicate by and between multiple independent implanted devices / modules. Alternatively, a wired network can be used to connect and enable communication by and between multiple independent implanted devices / modules. Each module can include the hardware, circuitry, memory, and processor described above as being included in the processing circuit 22. In addition, each module can include sufficient communication hardware and software stored in memory to enable communication between the modules. In this way, the modules can receive input from the individual input channels of the RPNI and coordinate to generate a system output for controlling a prosthetic device. For example, Figure 42 Three modules are shown: Module 1, Module 2, and Module 3. Module 1 and Module 2 are each connected to eight channels that receive input generated by the respective associated RPNI. Module 3 is connected to a first RPNI that generates input to Module 3 on two channels and to a second RPNI that generates input to Module 3 on a single channel.

[0206] Figure 43A and Figure 43B is a photograph of an RPNI with multiple sensing electrodes. Figure 43A shows an RPNI with patch electrodes on the back of the RPNI. Figure 43B shows an RPNI with patch electrodes on the front of the RPNI and also on the back of the RPNI.

[0207] Referring to Figure 44 , two bipolar patch electrodes are implanted on the same RPNI equidistant from the stimulating nerve cuff. In Figure 44 , patch 1 corresponds to the solid curves and patch 2 corresponds to the dashed curves, which show recordings taken from these two patch electrodes in the same recording window. The stimulation of the nerve cuff produced independent motor signaling with different waveforms and maximum amplitudes, which indicates that selective signals would need to be recorded on independent hardware channels to capture the different signals across the entire RPNI.

[0208] Referring to Figure 45 and Figure 46 , two sets of filament electrodes are implanted on the same RPNI equidistant from the stimulating nerve cuff. In Figure 45 , the dashed line shows the location of the RPNI. It can be seen that the sets of filament electrodes are attached to the RPNI. Referring to Figure 46 , similar to the results with patch electrodes, different waveforms are recorded from these two sets of electrodes. In Figure 46 , the graph shows one set of filament electrodes corresponding to the solid line and the second set of filament electrodes corresponding to the dashed line. Similar to the RPNI, the various structures described further below have independent motor units in the muscle tissue. Although some of the embodiments below include a single electrode in the muscle and a single electrode in the skin, the embodiments can similarly be extended to include multiple sensing electrodes instead of each single electrode to capture more independent signals.

[0209] Composite Regenerative Peripheral Nerve Interface (C-RPNI) Referring to Figure 47Another embodiment is shown, including a composite regenerative peripheral nerve interface (C-RPNI) structure 500 comprising a free skin or dermal graft 502 and a combination of a muscle graft 504 fixed around a target hybrid sensorimotor nerve 506 implanted between the dermal graft 502 and the muscle graft 504. Upon implantation, the nerve 506 exhibits preferential targeted renervation, such that sensory nerve fibers renervate the dermal graft 502, while motor nerve fibers renervate the muscle graft 504. In this way, the dermal graft renervates using sensory feedback nerve fibers connected to the sensorimotor nerve 506, and also renervates the muscle graft 504 using motor control nerve fibers connected to the sensorimotor nerve 506.

[0210] like Figure 47 As shown, stimulating electrodes 30a, 30b, and 30c are attached to the dermal graft 502 and provide stimulation signals, which are transmitted from the implantation device 20a to the sensory nerve fibers within the dermal graft 502 via wires 34a, 34b, and 34c. Similarly, sensing electrodes 14a, 14b, and 14c are attached to the muscle graft 504 and sense motor control signals from the motor control nerve fibers within the muscle graft 504. The sensing electrodes 14a, 14b, and 14c transmit the sensed motor control signals to the implantation device 20a via wires 18d, 18e, and 18f. Although Figure 47 The embodiments shown depict three stimulating electrodes 30a, 30b, 30c and three sensing electrodes 14a, 14b, 14c, but any number of sensing electrodes or stimulating electrodes according to this disclosure can be used. For example, according to this disclosure, a structure having one sensing electrode and one stimulating electrode can be used. Additionally, according to this disclosure, a structure having multiple sensing electrodes and only one stimulating electrode can be used. Furthermore, according to this disclosure, a structure having one sensing electrode and multiple stimulating electrodes can be used. Additionally, according to this disclosure, a structure having more than one (e.g., two, three, four, etc.) sensing electrode and more than one (e.g., two, three, four, etc.) stimulating electrode can be used.

[0211] Implant device 20a is similar to implant device 20 described above, except that implant device 20a includes separate processing circuitry for stimulation and for sensing. For example, implant device 20a includes stimulation processing circuitry 22a that is connected to leads 34a, 34b, 34c to transmit stimulation signals from implant device 20a to stimulation electrodes 30a, 30b, 30c. Implant device 20a also includes sensing processing circuitry 22b that is connected to leads 18d, 18e, 18f to receive sensed motion control signals from electrodes 14a, 14b, 14c. The motion control signals received by electrodes 14a, 14b, 14c can be used by sensing processing circuitry 22b and implant device 20a to perform motion control of the prosthetic device. Similarly, implant device 20a and stimulation processing circuitry 22a can receive sensory feedback information from the prosthetic device and generate and transmit stimulation signals that are transmitted to electrodes 30a, 30b, 30c to provide sensory feedback signals to sensory motor nerves 506. In this way, C-RPNI construct 500 can simultaneously amplify motion signals via muscle graft 504 while also providing sensory feedback via dermal graft 502.

[0212] According to the present disclosure, devices with multiple sensing electrodes can be used with multi-tissue RPNI grafts (i.e., C-RPNI with both dermal tissue and muscle tissue). For example, a single electrode implanted into muscle and a single electrode implanted into skin can be used independently to provide a simultaneous or real-time bidirectional interface. Multi-channel electrode devices can also be used to further enhance the selectivity of the interface, with multiple sensing electrodes recording independent signals from muscle and multi-contact stimulation electrodes (as described above) used to activate different fibers or perform current steering to the dermal tissue for selective sensory feedback.

[0213] Muscle cuff regenerative peripheral nerve interface (MC-RPNI) Additionally, although the above embodiments generally describe nerve endings placed within an RPNI or C-RPNI to reinnervate the muscle tissue of the RPNI or C-RPNI, any of the above embodiments can also be used with an RPNI or C-RPNI attached around an intact peripheral nerve. For example, any of the above embodiments can be used with a muscle cuff reinnervation peripheral nerve interface (MC-RPNI) having a structure with a free muscle graft and / or a free dermal graft circumferentially fixed to an intact peripheral nerve. The free muscle graft and / or free dermal graft are then reinnervated by the contained nerve and can amplify the nerve signals so as to reliably and accurately detect motor intent and transmit sensory feedback stimulation to the intact nerve. The MC-RPNI device can be used with an electrically powered exoskeleton device to restore function to a person with limb weakness. In this way, an RPNI or C-RPNI structure can be circumferentially attached around an intact nerve to create a MC-RPNI, whereby the intact nerve reinnervates the denervated muscle graft. The muscle graft of the RPNI amplifies signals that can be sensed with electrodes and used, for example, to control an exoskeleton. Additionally, the dermal graft can amplify stimulation signals to stimulate and provide sensory feedback to the intact nerve.

[0214] According to the present disclosure, a device with multiple sensing electrodes can also be used to record independent signals from a MC-RPNI circumferentially attached around a portion of a nerve rather than nerve endings. The MC-RPNI can be a single tissue structure (e.g., having only a muscle graft or a dermal graft) or a multi-tissue structure (e.g., having both a muscle graft portion and a dermal graft portion), and the device can provide a selective interface to control an exoskeleton or other prosthetic or medical device. In multi-tissue embodiments, the MC-RPNI structure can have a different geometry than the C-RPNI structure for nerve endings. For example, concentric wrapping of the tissues can be used in the MC-RPNI as compared to the "sandwich" type configuration in the nerve ending embodiments. Additionally, different geometries or implantation procedures can be used for multiple electrodes (e.g., multiple sensing electrodes and / or multiple stimulation electrodes).

[0215] Hardware and signal processing for C-RPNI and MC-RPNI Reference Figure 48 The implanted device 20a, stimulation processing circuit 22a, and sensing processing circuit 22b can be utilized and communicate with multiple C-RPNI structures 500. Although Figure 48The C-RPNI structure 500 is shown, but MC-RPNI structures can alternatively be used. The stimulation processing circuit 22a and the sensing processing circuit 22b can each include a stimulation front end and a sensing front end, respectively. The stimulation front end and the sensing front end can include sufficient modules, processors, programs, memories, circuits, and / or chips to perform the functions described in this disclosure as being performed by the stimulation processing circuit 22a and the sensing processing circuit 22b. Thus, the terms stimulation front end and sensing front end can be used interchangeably with the terms stimulation processing circuit 22a and sensing processing circuit 22b, respectively.

[0216] In this way, specialized hardware and signal processing can be used and performed for the stimulation electrodes and the sensing electrodes of each C-RPNI structure, each C-RPNI structure having one or more electrodes for stimulation and one or more electrodes for sensing. Having separate stimulation front ends and sensing front ends in the embedded system allows for a wider selection of ASICS that are fully optimized for low-noise sensing or enhanced stimulation functionality, as compared to systems having a single processing circuit that provides both sensing and stimulation functionality, such as the Intan RHS2116 chip described above.

[0217] Thus, according to the present disclosure, the implanted device 20a can stimulate the same nerve and record signals from the same nerve at the same time.

[0218] Reference is made to Figure 49 , the data shown was collected by alternating stimulation and recording on the same nerve. This process was repeated 100 times to verify signaling consistency. Figure 49 The constant compound muscle action potential (CMAP) on the right graph in Figure 49 shows that the muscle graft does not fatigue from simultaneous stimulation of the dermal side of the C-RPNI (as shown by the compound sensory nerve action potential (CSNAP) on the left graph in

[0219] The methods and algorithms discussed above with reference to Figure 31 to Figure 33 can be used with the embodiments for sensing and stimulation shown in Figure 47 and Figure 48 . For example, as described above with reference to Figure 31 and Figure 32 , the sensing signal from the muscle graft 504 can be ignored while the stimulation signal is transmitted to the dermal graft 502. However, in Figure 47 and Figure 48In the embodiments, separate and independent processing circuits / front ends (i.e., separate stimulation processing circuit 22a and sensing processing circuit 22b) and separate electrodes (i.e., separate sensing electrodes 14a, 14b, 14c and stimulation electrodes 30a, 30b, 30c) are used.

[0220] Simultaneous engagement was achieved using separate electrodes and processing circuitry / front-ends, but artifacts from the stimulus can still cause interference. However, Figure 47 and Figure 48 The embodiments described provide greater flexibility in mitigating artifacts. Figure 47 and Figure 48 The embodiments described above can also be referenced. Figure 33 The methods described above are used together to remove stimulus-induced artifacts through template / artifact extraction. By using the C-RPNI structure 500, the implanted device 20 and the sensing processing circuitry 22b can continue to sense and record signals from the same nerve stimulated by the stimulation processing circuitry 22a. In a single electrode and RPNI configuration, the RPNI would need to be excluded from sensing / recording when signals are sensed and recorded from other RPNIs / channels.

[0221] By using separate processing circuits (22a, 22b) for sensing and stimulation, the implantable device 20a can sense and record signals from all channels (e.g., all C-RPNIs or MC-RPNIs), while simultaneously and independently transmitting stimulation signals to the same C-RPNI or MC-RPNI. According to this disclosure, the implantable device, stimulation processing circuit 22a, and / or sensing processing circuit 22b can be configured to switch control parameters during operation depending on whether stimulation occurs simultaneously with sensing / recording. In this approach, the sensing processing circuit 22b can be programmed to implement a machine learning model with specific control parameters / weights, configured and trained to receive motor control neural signals from sensing electrodes 14a, 14b, 14c, and generate, for example, control signals for controlling a prosthesis. The machine learning model may, for example, include a neural network configured and trained with initial control parameters and weights, which are then adjusted during training of the machine learning model and the neural network.

[0222] Under this approach, the sensing processing circuit 22b can be configured to generate the control signals with different control parameters depending on whether the stimulation processing circuit 22a is simultaneously transmitting a perceptive stimulation signal to the stimulation electrodes 30a, 30b, 30c. For example, the sensing processing circuit 22b can be initialized with initial default control parameters. Once implanted in the patient and connected to the prosthetic device, the patient can be instructed to perform certain actions with the prosthesis. In the first phase of this process, the sensing processing circuit 22b can sense and record the generated motor signals from the nerves within the muscle graft 504 via the sensing electrodes 14a, 14b, 14c and generate control signals for controlling the prosthetic device. The control parameters used and stored by the sensing processing circuit 22b can then be updated based on feedback related to the control movements of the prosthetic device. In this way, the first phase of the process generates a first set of control parameters for use during time periods when the stimulation processing circuit 22a is not simultaneously providing stimulation signals to the skin graft 502 portion of the C-RPNI construct 500.

[0223] In the second phase, the patient can be instructed to perform the same actions with the prosthesis. However, in the second phase of the process, the stimulation processing circuit 22a is configured to provide stimulation signals to the skin graft 502 via the stimulation electrodes 30a, 30b, 30c at the same time that the sensing processing circuit 22b senses and records the generated motor signals from the nerves within the muscle graft 504 via the sensing electrodes 14a, 14b, 14c and generates control signals for controlling the prosthetic device. The control parameters used and stored by the sensing processing circuit 22b can then be updated based on feedback related to the control movements of the prosthetic device to generate a second set of control parameters. In this way, the second phase of the process generates a second set of control parameters for use during time periods when the stimulation processing circuit 22a is simultaneously providing stimulation signals to the skin graft 502 portion of the C-RPNI construct 500. This second set of control parameters beneficially and automatically accounts for any artifacts generated in the C-RPNI construct 500 due to the stimulation signals transmitted by the stimulation processing circuit 22a to the stimulation electrodes.

[0224] As a result of the first and second phases of the above training process, the sensing processing circuit 22b can store and utilize two separate sets of control parameters, including a first set of control parameters for use when the stimulation processing circuit 22a is not providing stimulation signals to the skin graft 502 of the C-RPNI construct 500 and a second set of control parameters for use when the stimulation processing circuit 22a is providing stimulation signals to the skin graft 502 of the C-RPNI construct 500.

[0225] In this way, the sensing processing circuit 22b can utilize one set of baseline "stimulation off' control parameters to receive signals from the sensing electrodes 14a, 14b, 14c when stimulation is not being performed, and utilize one set of "stimulation on' control parameters to receive signals from the sensing electrodes 14a, 14b, 14c when stimulation is being performed. Additionally, multiple sets of "stimulation on' control parameters can be generated and utilized to interpret different stimulation combinations. The "stimulation on' control parameters can enable all of the implanted devices 20a to maintain control over the prosthetic device in the presence of any stimulation artifacts caused by the stimulation processing circuit 22a. For example, this approach can beneficially avoid using alternating time windows as with other approaches described above with reference to Figure 31 and Figure 32 described above with reference to Figure 33 described above with reference to

[0226] Bidirectional interface with electrodes in different tissues for a composite regenerative peripheral nerve interface (C-RPNI) Stimulation of the RPNI promotes both kinesthetic sensation (e.g., movement) and cutaneous sensation. Furthermore, Figure 50 Stable perceptual detection thresholds of the patient's RPNI over time are shown. Figure 51 Hand mapping is shown that depicts the somatosensory accuracy of the involved sensations. However, the elicited cutaneous sensations are not described as feeling natural and are often described as tingling, buzzing, etc. This can be because the composition of the perceptual end organs is different from the skin and the perceptual fibers cannot be separated from the muscle fibers in the RPNI.

[0227] The C-RPNI consists of a transected nerve implanted between a muscle graft and a dermal graft. As described above, this allows the perceptual nerves to reinnervate the dermal side and the motor nerves to reinnervate the muscle side. Thus, motor signaling can be facilitated by electromyography (EMG) recordings from the muscle portion, but perceptual feedback can also be independently facilitated by stimulating the dermal portion. The C-RPNI can be used to record efferent motor signals or provide afferent perceptual feedback. However, many nerves in the body are mixed motor and sensory nerves, particularly at higher levels of amputation. In these cases, the use of multiple electrodes in the C-RPNI provides an interface for truly independent and near-simultaneous perceptual feedback and motor control.

[0228] Referring to Figure 52 , the bidirectional interface consists of a sensing electrode 520 implanted into the muscle tissue 504. As Figure 52As shown, the sensing electrode 520 can be a single unipolar electrode or a bipolar electrode. For example, the sensing electrode 520 may include a recording contact 524, such as... Figure 52 The two recording contacts 524 are shown. Alternatively, the sensing electrode 520 may include multiple electrodes or a high-resolution sensing electrode. One or more stimulation electrodes may also be implanted. For example, as Figure 52 As shown, the stimulation electrode 522 can be a multi-contact electrode with multiple stimulation contacts 526. A multi-contact electrode with more than two smaller electrode contacts is described in detail in US Patent No. 10,779,963, which is incorporated herein by reference in its entirety. Multiple sensing contacts selectively activate fibers near the dermal graft, enabling finer-resolution control over perceived location and sensory quality. The stimulation electrode 522 can also be a monopolar or bipolar electrode with larger contacts. However, a stimulation electrode 522 with larger contacts may inadvertently activate muscle tissue.

[0229] refer to Figure 53 The implantable device 20a may use a dedicated sensing ASIC and a dedicated stimulation ASIC optimized for low-noise sensing. For example, the implantable device 20a may include stimulation processing circuitry 22a and sensing processing circuitry 22b located on separate ASICs. Alternatively, refer to Figure 54 Alternatively, combined sensing and stimulation processing circuitry 540 can be used and integrated on a single ASIC. Typically, combined ASICs minimize size but offer lower performance and fewer functions compared to implementations utilizing separate ASICs for sensing and stimulation.

[0230] refer to Figure 55 The processing circuitry can be contained within a single implantable device 550. For example, as... Figure 55 As shown, the single implantable device 550 is a high-channel-count sensing and stimulation implantable device. (Reference) Figure 56 The processing circuitry can be distributed across a network of multiple implanted devices / modules within the system. Existing embodiments of networked implants can be utilized. For example, wireless body domain networks (BANs) or wired network implants can be used. The network architecture can be used to increase the total system channel count and can be organized geographically (e.g., ...). Figure 56 (as shown), or organized by function (such as) Figure 57 (As shown). Each module may include the hardware, circuitry, memory, and processor described above and included in processing circuitry 22. Additionally, each module may include sufficient communication hardware and software stored in memory to enable communication between modules. In this way, the modules can receive inputs from the various input channels of the C-RPNI 500 and coordinate to generate system outputs for controlling the prosthetic device. For example, Figure 56Three modules are shown: Module 1, Module 2, and Module 3, each including sensing and stimulation processing circuitry to perform sensing and stimulation functions via multiple sensing and stimulation channels connected to each C-RPNI 500. Conversely, Figure 57 This includes a sensing module 1 and a stimulation module 1. Sensing module 1 is connected to a bus that connects to the sensing channel of each C-RPNI 500, while stimulation module 1 is connected to a separate bus that connects to the stimulation channel of each C-RPNI 500. Alternatively, a single bus can be used to control both modules and all C-RPNIs.

[0231] For each embodiment, different techniques can be applied to mitigate stimulation artifacts in a truly bidirectional interface. For example, as described above, one technique involves rapidly alternating stimulation and recording windows. This technique achieves near-simultaneous incoming and outgoing signaling. To minimize significant latency in the prosthesis control system, the sum of each stimulation block and recording window should be less than 200 ms (ideally less than 100 ms), but other time intervals can also be used. The stimulation and recording windows do not need to be of equal length. Instead, the stimulation window can be minimized as much as possible while eliminating the corruption of outgoing signal measurements due to artifacts. If other signal filtering or artifact subtraction techniques successfully remove stimulation artifacts, the stimulation window can be completely eliminated to achieve truly simultaneous control.

[0232] like Figure 58 The image shown is a photograph of a C-RPNI structure that includes a transverse nerve implanted between a dermal graft 580 and a muscle graft 582.

[0233] Figure 59 Photographs of the electrodiagnostic setup are shown, in which afferent signaling is generated and recorded by stimulating the dermal graft with patch electrodes and recording afferent activity using a neural cuff on the proximal CP nerve. Muscle signals are generated and recorded by stimulating the proximal CP nerve with a neural cuff and recording EMG signals using bipolar intramuscular electrodes in the muscle graft.

[0234] In each C-RPNI, the dermal graft naturally attracts sensory fibers, which are connected using dedicated stimulation electrodes and stimulation tips (i.e., stimulation processing circuitry). The muscle graft is naturally re-innervated by motor fibers, which are connected using dedicated recording electrodes and sensing tips (i.e., sensing processing circuitry).

[0235] C-RPNI enables simultaneous stimulation and recording of the same nerve at the same time. (Reference curve) Figure 60 The curve Figure 60This is a graph showing the data collected by alternating stimulation and recording of the same nerve. This was repeated 1000 times to verify signaling consistency. Figure 60 The constant CMAP shown demonstrates that muscles do not fatigue from stimulation of the dermal side of C-RPNI.

[0236] Composite Cuff Regeneration Peripheral Nerve Interface (CC-RPNI) An ideal control system for an electrically powered exoskeleton would respond to the user's intentions by utilizing signals directly from the nervous system. A direct interface for acquiring motor intentions from the nervous system is crucial because the exoskeleton must generate movement with minimal latency to coordinate functional muscles that provide partial mobility. Sensory feedback is an important part of controlling both the upper and lower limbs. Patients with intact nerves may retain only partial motor function and partial sensory feedback in their affected limbs. Therefore, in these cases, a dual interface is required for full functional recovery. The Composite Cuff Regenerative Peripheral Nerve Interface (CC-RPNI) is ideal for this dual functionality because it includes a dermal portion that can be re-innervated by sensory fibers for sensory feedback and a muscular portion that amplifies the motor signal to detect motor intentions.

[0237] The electrode interface, implantable device, and processing circuitry used in the CC-RPNI structure are similar to those described above in the bidirectional interface with electrodes in different tissues for the Composite Regenerative Peripheral Nerve Interface (C-RPNI). However, in the case of the CC-RPNI structure, the multi-tissue graft is created on intact nerves to preserve any remaining downstream function.

[0238] like Figure 61 As shown, the CC-RPNI structure includes a free muscle graft 504 and a free dermal graft 502, which are circumferentially fixed to intact peripheral nerves 610. The free muscle graft 504 and free dermal graft 502 are then re-innervated by the included nerves, and the neural signals can be amplified to reliably and accurately detect motor intentions and transmit sensory feedback.

[0239] Similar to the discussion of the C-RPNI structure above, the bidirectional interface consists of sensing electrodes 520 implanted in muscle tissue 504. For example... Figure 61 As shown, the sensing electrode 520 can be a single unipolar electrode or a bipolar electrode. For example, the sensing electrode 520 may include a recording contact 524, such as... Figure 61 The two recording contacts 524 are shown. Alternatively, the sensing electrode 520 may include multiple electrodes or a high-resolution sensing electrode. One or more stimulation electrodes may also be implanted. For example, as Figure 61As shown, the stimulating electrode 522 can be a multi-contact electrode with multiple stimulating contacts 526. A multi-contact electrode with more than two small electrode contacts is described in detail in U.S. Patent No. 10,779,963, which is incorporated by reference herein in its entirety. The multiple sensing contacts selectively activate fibers near the dermal graft, which enables more fine resolution control of perceived location and quality of sensation. The stimulating electrode 522 can also be a monopolar electrode or a bipolar electrode with larger contacts. However, stimulating electrodes 522 with larger contacts can inadvertently activate muscle tissue.

[0240] Referring to Figure 62 , a photograph of a CC-RPNI construct 626 is shown. The CC-RPNI construct 626 includes a muscle graft 620 wrapped circumferentially around an intact nerve 622. A dermal graft 624 is secured to the muscle graft 620 around the intact nerve 622.

[0241] Referring to Figure 63 , afferent signaling was generated and recorded by stimulating the dermal graft with a patch electrode and recording afferent activity with a nerve cuff on the proximal CP nerve. Muscle signals were generated and recorded by stimulating the proximal CP nerve with a nerve cuff and recording EMG signals with a filament electrode in the muscle graft.

[0242] Referring to Figure 64 , a graph shows afferent signaling and efferent signaling of a CC-RPNI. Figure 64 The left-hand side graph in shows successful reinnervation of the dermal portion of the graft for afferent sensory signaling. Electrical stimulation of the dermal graft produced a CSNAP recorded upstream from the nerve cuff. Figure 64 The right-hand side graph in shows successful reinnervation of the muscle portion of the graft for efferent motor signals. Stimulation of the nerve cuff produced a downstream CMAP recorded from the muscle.

[0243] Muscle cuff and skin regenerative peripheral nerve interface (RPNI) with electrodes in muscle and skin Commonly implanted electrodes have larger contacts relative to the size of the RPNI construct and C-RPNI construct. Using a construct with larger electrodes can produce a larger activation field, which can inadvertently activate muscle and nerve fibers. Referring to Figure 65, showing a setup for electrophysiology experiments on a C-RPNI 650 construct, including a dermal graft 654 and a muscle graft 656 attached to a nerve 658. Using the setup shown, a stimulating dermal electrode 651 is configured to transmit a stimulating electrical signal to the dermal graft 654, and a recording muscle electrode 652 is configured to record electrical signals from the muscle graft 656. A stimulating and recording nerve cuff electrode 653 is configured to detect a recorded sensor feedback electrical signal from the nerve 658, such as a signal transmitted through the dermal graft 654. The stimulating and recording nerve cuff electrode 653 is also configured to transmit a direct motor intent electrical signal to the nerve 658. The recording muscle electrode 652 can then record electrical signals transmitted from the stimulating and recording nerve cuff electrode 653 to the nerve 658, and then to the muscle graft 656.

[0244] Reference is made to Figure 66 , showing signal recordings using the setup shown in Figure 65 . Figure 66 The plots on the left-hand side in Figure 66 , for example, show nerve cuff recordings and muscle graft recordings using the stimulating dermal electrode 651 to stimulate the dermal graft 654. However, as evidenced by the large amplitude compound motor action potentials shown by the lower left plot in Figure 66 , the muscle graft 656 is also electrically stimulated activated. Figure 66 The amplitude of this activation shown by the lower left plot in Figure 66 is comparable to the activity generated by the efferent nerve signals shown by the lower right plot in Figure 66 The plots on the right-hand side in Figure 66 , the muscle graft recordings are similar when stimulation is provided by the nerve 658 and when stimulation is provided to the dermal graft 654. As noted above, near simultaneous engagement can be achieved without fatiguing the muscle tissue. However, this unexpected activation can have adverse clinical effects, such as when the intent is to produce only controlled somatosensory (such as haptic feedback), but uncontrolled motor sensations are produced. The uncontrolled sensations can confuse and degrade the quality of the sensory feedback. In addition, adverse clinical effects can include muscle activation by the electrical stimulation, which can confuse volitional motor commands. As noted above, the problem can be mitigated using a multi-contact sensing electrode.

[0245] To address these problems, the sensing interface and the stimulating interface can be geometrically separated to enable the use of larger, simpler electrodes that can stimulate the afferent sensory fibers without affecting muscle activation.

[0246] Referring to Figure 66 , a construct is shown having a single tissue cuff circumferentially wrapped around a portion of a nerve 670, and an RPNI having a different tissue located at the distal end of the nerve 670. For example, a free muscle graft cuff 672 is attached circumferentially around the nerve 670, while a free dermal graft 674 is attached to the distal end of the nerve 670. A sensing electrode 520 is implanted into the muscle tissue 672. As shown in Figure 67 , the sensing electrode 520 can be a single monopolar electrode or a bipolar electrode. For example, the sensing electrode 520 can include a recording contact 524, such as the two recording contacts 524 shown in Figure 67 . Alternatively, the sensing electrode 520 can include multiple electrodes or a high resolution sensing electrode. One or more stimulating electrodes can also be implanted. For example, as shown in Figure 67 , the stimulating electrode 522 can be a multi-contact electrode implanted into the dermal graft 674 with multiple stimulating contacts 526. A multi-contact electrode with more than two small electrode contacts is described in detail in U.S. Patent No. 10,779,963, which is incorporated by reference herein in its entirety. The multiple sensing contacts selectively activate fibers near the dermal graft, which enables more fine resolution control of the perceived location and quality of sensation. The stimulating electrode 522 can also be a monopolar or bipolar electrode with a larger contact. However, a stimulating electrode 522 with a larger contact can inadvertently activate the muscle tissue.

[0247] As shown in Figure 67 , the free muscle graft 672 is physically and geometrically separated on the nerve 670, creating a gap 676 and physical separation between the muscle graft 672 and the dermal graft 674 along the axis of the nerve 670. The gap 676 between the two tissues geometrically isolates the interface, minimizing signaling interference. In this example, a muscle cuff is created with the muscle graft 672 located on an intact portion of the nerve proximal to the dermal graft 674 created at the distal end of the nerve, with the muscle graft 672 and the dermal graft 674 separated by the physical gap 676 along the axis of the nerve 670. Motor fibers reinervate the motor end-plate on the muscle graft 672 of the muscle cuff, while sensory fibers reinervate the dermal graft 674 at the distal end of the nerve 670. Motor fibers at the distal end of the nerve have no reinervation targets in the dermal graft and are pruned.

[0248] Referring to Figure 67The structure of this embodiment geographically separates the motor interface and the sensory interface along the axis of the nerve. This greatly improves the isolation between the motor and sensory portions of the tissue interface and enables independent activation of sensory and motor functions with independent electrodes. Amplified efferent motor signals are recorded from the muscle cuff portion of the structure and the afferent sensory feedback is delivered through the skin graft on the distal end of the severed nerve 670.

[0249] Reference is made to Figure 68 , which shows signal recordings using the structures in Figure 69 and Figure 67 . For example, the top left graph in Figure 68 shows a plot of an afferent nerve action potential generated by stimulation of the dermal graft 674. Unlike the other structures, no motor unit action potentials are observed in the muscle graft 672, as shown by the bottom left graph in Figure 69 , establishing isolation. In other words, with the structures in Figure 69 and Figure 67 , the muscle graft 672 is isolated from the stimulation signals provided to the dermal graft 674. However, as shown by the bottom right graph in Figure 68 , efferent motor signaling from the nerve does generate motor unit action potentials of larger amplitude in the muscle cuff tissue. Similar to the C-RPNI, the stability of the skin RPNI and muscle cuff was also verified with 1000 repetitions of afferent and efferent signaling, as shown by the graph in Figure 69 Figure 70 .

[0250] Non-limiting discussion of terms The foregoing description of various embodiments has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form disclosed, and various modifications and variations are possible in light of the above teachings or can be acquired from practice of the technology. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable with other embodiments and can be used in other examples, even if not specifically shown or described. The same can hold true for the description of examples with similar functionalities. Such variations are not to be regarded as a departure from the scope of the disclosure, and all such modifications are intended to be included within the scope of the disclosure. Examples are presented in order to provide a thorough and enabling disclosure of the technology, and to fully convey the scope of the disclosure to those skilled in the art. Numerous specific details are set forth in order to provide a thorough understanding of the examples of the technology. Those skilled in the art will realize, however, that the technology can be practiced without the specific details set forth in this description, in a variety of different ways, and that the technology is not limited to the examples described in detail herein. In some examples, well-known processes, well-known device structures, and well-known technologies are not described in detail in order to avoid obscuring the disclosure.

[0251] As used herein, the term circuitry can refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); an electronic circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor (shared, dedicated, or group) that executes code; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip. The term circuitry can include memory (shared, dedicated, or group) that stores code for execution by the processor(s).

[0252] As used above, the term code can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, and / or objects. As used above, the term shared implies some or all code from multiple circuits can execute using a single (shared) processor. In addition, some or all code from multiple circuits can be stored by a single (shared) memory. As used above, the term group implies some or all code from a single circuit can execute using a group of processors. In addition, some or all code from a single circuit can be stored using a group of memories.

[0253] The apparatus and methods described herein can be implemented by one or more computer programs executed by one or more processors. The computer programs include processor-executable instructions stored on a non-transitory computer-readable medium. The computer programs can also include stored data. Non-limiting examples of non-transitory computer-readable media are nonvolatile memory, magnetic storage, and optical storage.

[0254] Headings (e.g., “BACKGROUND,” and “SUMMARY” and sub-headings) and sub-headings, if any, are used herein for clarity spanning five paragraphs of text merely to provide organizational structure for a broad disclosure. They are not intended to limit the scope of this disclosure or any embodiments thereof. Specifically, the subject matter disclosed in the “BACKGROUND” can include novel-technology and can not constitute a recitation of prior art. The subject matter disclosed in the “SUMMARY” is not an exhaustive or complete disclosure of all the ranges of this technology or any embodiments thereof. Classifying or discussing a material within a portion of this specification as having a certain use is done for convenience, and should not be taken as an attempt to limit the material to only that use when the material is used in any given composition.

[0255] The disclosures of all patents and patent applications cited herein are hereby incorporated by reference in their entirety.

[0256] The description and specific examples, while indicating particular features and embodiments, are intended for illustration and description only and are not intended to limit the scope of the disclosure. Furthermore, the listing of multiple embodiments having described features does not preclude other embodiments having additional features or different combinations of the described features. Specific examples are provided for purposes of illustration and description, and are not intended to represent the only embodiments in which the described methods, systems, and compositions can be practiced, unless otherwise indicated.

[0257] As used herein, the word "prefer" or "preferably" indicates an embodiment that provides certain benefits under some circumstances. However, other embodiments can also be preferred under the same or other circumstances. Furthermore, listing one or more preferred embodiments does not imply that other embodiments are not useful and are not intended to exclude other embodiments from the scope of the disclosure.

[0258] As used herein, the word "include" and its variants are intended to be non-limiting, such that recitation of items in a list does not imply that additional similar items are not also useful in the methods, systems, materials, compositions, and apparatus described. Similarly, the terms "can" and "may" and their variants are intended to be non-limiting, such that recitation that an embodiment can or might include certain elements or features does not exclude other embodiments that do not include such elements or features.

[0259] While the embodiments of the disclosure are described and claimed using the open-ended term "comprise" as synonymous with other non-limiting terms such as include, contain, or have, embodiments can alternatively be described using more limiting terms such as "consist of or "consist essentially of. Thus, for any given embodiment reciting a composition, material, or process step, the disclosure also specifically includes embodiments where the composition, material, or process consists of or consists essentially of the recited elements, excluding additional elements of material, components or processes (for compositions consisting of) and excluding additional material, components or processes affecting the substantial characteristics of the embodiment (for compositions consisting essentially of), even if such additional elements are not explicitly recited in the application. For example, recitation of a composition or process comprising elements A, B, and C specifically contemplates embodiments consisting of A, B, and C and embodiments consisting essentially of A, B, and C, excluding element D, even if element D is not explicitly described as being excluded in the text of the application.

[0260] As referred to herein, ranges include endpoints and include all intervening values and further divided ranges within the range. Thus, for example, a range from A to B or from about A to about B includes A and B. Disclosure of a value and a range of values for a particular parameter (e.g., temperature, molecular weight, weight percent, etc.) is not exclusionary of other values and ranges of values useful herein. Use of the term“about” in reference to a range, value or threshold should be considered in the context of the range, value or threshold. To the extent a range, value or threshold cannot be determined from the context, use of the term“about” can correspond to a range of ten to fifteen percent. It is contemplated that two or more specific example values for a given parameter can define endpoints of a range of values for that parameter that can be claimed. For example, if a parameter X is exemplified herein as having a value of A, and also exemplified as having a value of Z, it is contemplated that parameter X can have a range of values from about A to about Z. Similarly, it is contemplated that disclosure of two or more ranges of values for a parameter, whether those ranges are nested, overlapping or distinct, includes all possible combinations of ranges of values that can be claimed using the endpoints of the disclosed ranges. For example, if a parameter X is exemplified herein as having a value in a range of 1 to 10, or 2 to 9, or 3 to 8, it is also contemplated that parameter X can have other ranges of values, including 1 to 9, 1 to 8, 1 to 3, 1 to 2, 2 to 10, 2 to 8, 2 to 3, 3 to 10, and 3 to 9.

Claims

1. A system comprising: an implant device having processing circuitry configured to receive electrical signals from a free tissue graft surgically attached to a nerve of a subject; wherein the electrical signals are received by a plurality of electrodes attached to and in electrical communication with the free tissue graft; the free tissue graft is an autograft of tissue de-vascularized and de-innervated prior to being surgically attached to the subject such that the free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the processing circuitry is further configured to process the received electrical signals from the plurality of electrodes, generate processed signal data corresponding to the received electrical signals from each of the plurality of electrodes, and transmit the processed signal data to a prosthesis controller; wherein: the nerve re-innervates the plurality of free tissue grafts after the plurality of free tissue grafts are surgically attached to the nerve; the prosthesis controller is configured to control a prosthesis device based on the processed signal data transmitted from the processing circuitry of the implant device; and the prosthesis controller controls the prosthesis device to perform a plurality of different functions based on the processed signal data generated based on the received electrical signals from each of the plurality of electrodes.

2. The system of claim 1, wherein, each of the plurality of electrodes is configured to sense electrical signals from a different region of a plurality of regions of the free tissue graft.

3. The system of claim 2, wherein, the plurality of regions map to the plurality of different functions of the prosthesis device such that each of the plurality of regions is associated with a different one of the plurality of functions; and wherein the prosthesis controller is configured to control the prosthesis device to perform the plurality of different functions based on the associated region of the free tissue graft from which the electrical signals are received.

4. The system of claim 1, wherein, at least one of the plurality of electrodes has a diameter of less than 1.5 millimeters, a recording contact length of 1 centimeter to 2 centimeters, and a bipolar inter-contact spacing of less than 2 centimeters.

5. The system of claim 1, wherein, the plurality of electrodes have a recording contact length of less than 2 millimeters.

6. The system of claim 1, wherein, at least one of the plurality of electrodes is a high resolution electrode having eight contacts each having a recording contact length of 0.2 millimeters or less.

7. The system of claim 1, wherein, The processing circuitry is configured to receive additional electrical signals, the additional electrical signals being received from a plurality of additional electrodes attached to an additional free tissue graft, the additional free tissue graft being surgically attached to an additional nerve of the subject; wherein the additional electrical signals are received by a plurality of additional electrodes attached to and in electrical communication with the additional free tissue graft; the additional free tissue graft being surgically attached to the subject such that the additional free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the additional free tissue graft being an additional autograft of tissue that is devascularized and denervated prior to being surgically attached to the subject.

8. The system of claim 1, wherein: the processing circuitry comprises a plurality of modules in communication with one another; a first module of the plurality of modules receives the electrical signals from the plurality of modules; a second module of the plurality of modules receives additional electrical signals, the additional electrical signals being received from a plurality of additional electrodes attached to an additional free tissue graft, the additional free tissue graft being surgically attached to an additional nerve of the subject; wherein the additional electrical signals are received by a plurality of additional electrodes attached to and in electrical communication with the additional free tissue graft; the additional free tissue graft being surgically attached to the subject such that the additional free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the additional free tissue graft being an additional autograft of tissue that is devascularized and denervated prior to being surgically attached to the subject; the first module and the second module are configured to communicate with one another.

9. The system of claim 8, wherein, the first module and the second module are configured to communicate with one another via at least one of a wireless body area network (BAN) or a wired network implanted within the subject.

10. The system of claim 1, wherein, the electrical signals from the free tissue graft have a voltage amplitude greater than or equal to about 150 microvolts.

11. A method comprising: a processing circuitry of an implanted device receiving electrical signals from a free tissue graft, the free tissue graft being surgically attached to a nerve of a subject; wherein the electrical signals are received by a plurality of electrodes attached to and in electrical communication with the free tissue graft; the free tissue graft being surgically attached to the subject such that the free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the free tissue graft being an autograft of tissue that is devascularized and denervated prior to being surgically attached to the subject; processing, with the processing circuitry, the received electrical signals from the plurality of electrodes; generating, with the processing circuitry, processed signal data corresponding to the received electrical signals from each of the plurality of electrodes; transmitting, with the processing circuit, the processed signal data to a prosthesis controller; wherein: after the plurality of free tissue grafts are surgically attached to the nerve, the nerve reinnervates the plurality of free tissue grafts; the prosthesis controller is configured to control a prosthesis device based on the processed signal data transmitted from the processing circuit of the implant device; and the prosthesis controller controls the prosthesis device to perform a plurality of different functions based on the processed signal data generated based on the received electrical signals from each of the plurality of electrodes.

12. The method of claim 11, wherein, each of the plurality of electrodes is configured to sense electrical signals from a different region of a plurality of regions of the free tissue graft.

13. The method of claim 12, wherein, the plurality of regions map to the plurality of different functions of the prosthesis device such that each of the plurality of regions is associated with a different one of the plurality of functions; and wherein the prosthesis controller is configured to control the prosthesis device to perform the plurality of different functions based on the associated region of the free tissue graft from which the electrical signals are received.

14. The method of claim 11, wherein, at least one of the plurality of electrodes has a diameter of less than 1.5 millimeters, a recording contact length of 1 centimeter to 2 centimeters, and a bipolar inter-contact spacing of less than 2 centimeters.

15. The method of claim 11, wherein, the plurality of electrodes has a recording contact length of less than 2 millimeters.

16. The method of claim 11, wherein, at least one of the plurality of electrodes is a high resolution electrode having eight contacts each having a recording contact length of 0.2 millimeters or less.

17. The method of claim 11, further comprising: receiving, with the processing circuit, additional electrical signals, the additional electrical signals being received from a plurality of additional electrodes attached to an additional free tissue graft, the additional free tissue graft being surgically attached to an additional nerve of the subject; wherein the additional electrical signals are received by a plurality of additional electrodes attached to and in electrical communication with the additional free tissue graft; the additional free tissue graft being an additional autograft of tissue harvested from the subject, devascularized, and denervated prior to being surgically attached to the subject such that the additional free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject.

18. The method of claim 11, wherein: the processing circuit comprises a plurality of modules in communication with one another; a first module of the plurality of modules receives the electrical signals from the plurality of modules; a second module of the plurality of modules receives additional electrical signals, the additional electrical signals being received from a plurality of additional electrodes attached to an additional free tissue graft, the additional free tissue graft being surgically attached to an additional nerve of the subject; wherein the additional electrical signals are received by a plurality of additional electrodes attached to and in electrical communication with the additional free tissue graft; the additional free tissue graft is surgically attached to the subject such that the additional free tissue graft is completely surrounded by and in direct contact with non-grafted tissue of the subject; the additional free tissue graft is an additional autograft of tissue that is devascularized and denervated prior to being surgically attached to the subject; the first module and the second module are configured to communicate with each other.

19. The method of claim 18, wherein, the first module and the second module are configured to communicate with each other via at least one of a wireless body area network (BAN) or a wired network implanted within the subject.

20. The method of claim 11, wherein, the electrical signals from the free tissue graft have a voltage amplitude greater than or equal to about 150 microvolts. the first module and the second module are configured to communicate with each other via at least one of a wireless body area network (BAN) or a wired network implanted within the subject. the electrical signals from the free tissue graft have a voltage amplitude greater than or equal to about 150 microvolts.

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