Systems and methods for controlling prosthetic devices based on amplified neural signals

Abstract

The present disclosure provides a method and system for receiving electrical signals from a free tissue graft affixed to a nerve at a processing circuit of an implant device through a pair of bipolar electrodes, each having a positive and negative electrode, in electrical communication with the free tissue graft. The free tissue graft is an autograft of tissue that is removed from the subject, devascularized, denervated, and then surgically affixed to the subject such that the free tissue graft is completely surrounded by and in direct contact with the non-implanted tissue of the subject. The electrical signals from the free tissue graft have a voltage amplitude of about 150 microvolts or greater. The processing circuit processes the electrical signals and transmits them wirelessly to a prosthetic controller that controls the prosthetic device.

Description

[Technical field]

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 314,531, filed February 28, 2022. The entire disclosure of the above application is incorporated herein by reference.

[0002] The present disclosure relates to systems and methods for controlling prosthetic devices based on amplified nerve signals, and more particularly, to systems and methods for controlling prosthetic devices based on amplified signals from individual nerve bundles in an implantable regenerative peripheral nerve interface device, as well as systems and methods for processing the amplified signals to control a prosthetic device. [Background technology]

[0003] This section provides background information relevant to the present disclosure that is not necessarily prior art.

[0004] There is a need to receive and record signals from a nerve (e.g., a human nerve) for later processing and use, such as in controlling a prosthetic limb. A free tissue graft can be affixed to a portion of the nerve, such as a nerve bundle, and electrical signals from the nerve can be amplified by the free tissue graft. Nevertheless, there is a need for systems and methods for effectively and efficiently processing the amplified signals from the nerve, and for effectively and efficiently controlling a prosthetic device based on the processed signals. Additionally, there is a need for systems and methods for effectively and efficiently transmitting sensory feedback signals from the prosthetic device to the nerve through the free tissue graft. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2013 / 0304174 Summary of the Invention [Means for solving the problem]

[0006] This section provides a general overview of the disclosure, but is not an exhaustive disclosure of its full scope or all of its features.

[0007] The present disclosure provides a method and system for receiving electrical signals from a free tissue graft affixed to a nerve at a processing circuit of an implant device through a pair of bipolar electrodes, each having a positive and negative electrode, in electrical communication with the free tissue graft. The free tissue graft is an autograft of tissue that is removed from the subject, devascularized, denervated, and then surgically affixed to the subject such that the free tissue graft is completely surrounded by and in direct contact with the non-implanted tissue of the subject. The electrical signals from the free tissue graft have a voltage amplitude of about 150 microvolts or greater. The processing circuit processes the electrical signals and transmits them wirelessly to a prosthetic controller that controls the prosthetic device.

[0008] The present disclosure includes a system comprising an implant device having a processing circuit configured to receive electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of a subject, the electrical signals being received through a plurality of pairs of bipolar electrodes, each having a positive electrode and a negative electrode, implanted inside the plurality of free tissue grafts and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being autografts of tissue that have been removed from the subject, devascularized, denervated, and then surgically affixed to the subject, the processing circuit further configured to process the received electrical signals, generate processed signal data, and wirelessly transmit the processed signal data to a prosthetic controller. The nerve is a reinnervated version of the plurality of free tissue grafts after the plurality of free tissue grafts have been surgically affixed to the nerve. The electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater. The prosthetic controller is configured to control the prosthetic device based on the processed signal data wirelessly transmitted from the processing circuitry of the implant device.

[0009] In other features, the processing circuitry is configured to apply a bandpass filter to the received electrical signal and sample the filtered electrical signal.

[0010] In other features, the bandpass filter is a 100 to 500 Hz bandpass filter, and the processing circuitry is configured to sample the filtered electrical signal at a sampling rate of 1 kHz.

[0011] In other features, the processing circuitry is further configured to generate processed signal data by calculating a mean absolute value of the sampled electrical signals, and wirelessly transmit the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic controller.

[0012] In other features, the implant device includes protection circuitry for protecting the implant device from surge voltages during an electrostatic discharge event during implantation of the implant device within a patient.

[0013] In other features, the protection circuit includes at least one resistor and at least one diode.

[0014] In other features, the plurality of bipolar electrode pairs includes a first set of bipolar electrode pairs and a second set of bipolar electrode pairs, the first and second sets of bipolar electrode pairs respectively connected to the implant device with first and second multi-contact connectors each having a pair of contacts associated with each bipolar electrode pair, and the implant device includes headers having first and second ports configured to receive the first and second multi-contact connectors, respectively.

[0015] In other features, the first and second ports include Bal Seal connectors configured to seal the implant device 20 when the first and second multi-contact connectors are inserted into the first and second ports, respectively.

[0016] In other features, the prosthetic device includes at least one pressure sensor, the prosthetic controller is configured to receive at least one pressure signal from the at least one pressure sensor and communicate the pressure signal to the implant device, and a processing circuit of the implant device is further configured to receive the pressure signal and generate and transmit at least one stimulation signal to at least one of the plurality of bipolar electrode pairs to stimulate the subject's nerve through at least one of the free tissue grafts affixed to the subject's nerve.

[0017] In other features, the processing circuitry of the implant device is configured to alternate between receiving the electrical signal and generating and transmitting at least one stimulation signal during successive time periods.

[0018] In other features, the processing circuitry is configured to generate commands for controlling the prosthetic device based on the received electrical signals, and generate estimated commands for controlling the prosthetic device during a period when the at least one stimulation signal is generated and transmitted to at least one of the plurality of bipolar electrode pairs.

[0019] In other features, the processing circuitry is configured to estimate an artifact in the electrical signal produced by the at least one stimulation signal and subtract the artifact from the electrical signal.

[0020] The present disclosure also includes a method including receiving electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of a subject with an implant device having a processing circuit and a communication circuit, the electrical signals being received through a plurality of pairs of bipolar electrodes each having a positive electrode and a negative electrode implanted inside and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being autografts of tissue that are removed from the subject, devascularized, denervated, and then surgically affixed to the subject. The method further includes processing the received electrical signals with the processing circuit. The method further includes generating processed signal data with the processing circuit. The method further includes wirelessly transmitting the processed signal data to a prosthetic controller with the communication circuit. The portion of the nerve is a reinnervation of the plurality of free tissue grafts after the plurality of free tissue grafts are surgically affixed to the nerve. The electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater. The prosthetic controller is configured to control the prosthetic device based on the processed signal data wirelessly transmitted from the processing circuitry of the implant device.

[0021] In other features, the method also includes applying a bandpass filter to the received electrical signal with the processing circuitry and sampling the filtered electrical signal with the processing circuitry.

[0022] In other features, the bandpass filter is a 100 to 500 Hz bandpass filter and the sampling is performed at a sampling rate of 1 kHz.

[0023] In other features, the method further includes generating processed signal data in the processing circuitry by calculating a mean absolute value of the sampled electrical signals and wirelessly transmitting the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic device controller in the communications circuitry.

[0024] In other features, the implant device includes protection circuitry for protecting the implant device from surge voltages during an electrostatic discharge event during implantation of the implant device within a patient.

[0025] In other features, the protection circuit includes at least one resistor and at least one diode.

[0026] In other features, the plurality of bipolar electrode pairs includes a first set of bipolar electrode pairs and a second set of bipolar electrode pairs, the first and second sets of bipolar electrode pairs respectively connected to the implant device with first and second multi-contact connectors each having a pair of contacts associated with each bipolar electrode pair, and the implant device includes headers having first and second ports configured to receive the first and second multi-contact connectors, respectively.

[0027] In other features, the first and second ports include Bal Seal connectors configured to seal the implant device 20 when the first and second multi-contact connectors are inserted into the first and second ports, respectively.

[0028] In other features, the prosthetic device includes at least one pressure sensor, and the method further includes receiving at least one pressure signal from the at least one pressure sensor at the prosthetic controller, communicating the pressure signal at the prosthetic controller to the implant device, receiving the pressure signal at a processing circuit of the implant device, and generating at least one stimulation signal at the processing circuit of the implant device and transmitting to at least one of the plurality of bipolar electrode pairs to stimulate a nerve of the subject through at least one of the free tissue grafts affixed to the nerve of the subject.

[0029] In other features, the processing circuitry of the implant device is configured to alternate between receiving the electrical signal and generating and transmitting at least one stimulation signal during successive time periods.

[0030] In other features, the method further includes generating, at a processing circuitry of the implant device, commands for controlling the prosthetic device based on the received electrical signals, and generating, at a processing circuitry of the implant device, estimated commands for controlling the prosthetic device during a period in which at least one stimulation signal is generated and transmitted to at least one of the plurality of bipolar electrode pairs.

[0031] In other features, the processing circuitry is configured to estimate an artifact in the electrical signal produced by the at least one stimulation signal and subtract the artifact from the electrical signal.

[0032] The present disclosure further comprises a system comprising a computing device having a processing circuit configured to receive electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of a subject, the electrical signals being received through a pair of a plurality of bipolar electrodes, each having a positive electrode and a negative electrode, implanted inside the plurality of free tissue grafts and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being autografts of tissue that have been removed from the subject, devascularized, denervated, and then surgically affixed to the subject, the processing circuit further configured to process the received electrical signals, generate processed signal data, and transmit the processed signal data to a prosthetic controller. A portion of the nerve is reinnervated from the plurality of free tissue grafts after the plurality of free tissue grafts have been surgically affixed to the nerve. The electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater. The prosthetic controller is configured to control the prosthetic device based on the processed signal data transmitted from the processing circuitry of the computing device. The plurality of bipolar electrode pairs are connected to electrical leads extending outside the subject and connected to a computing device placed outside the subject, the computing device being in communication with the prosthetic controller.

[0033] In other features, the processing circuitry is configured to apply a bandpass filter to the received electrical signal and sample the filtered electrical signal.

[0034] In other features, the bandpass filter is a 100 to 500 Hz bandpass filter, and the processing circuitry is configured to sample the filtered electrical signal at a sampling rate of 1 kHz.

[0035] In other features, the processing circuitry is further configured to generate processed signal data by calculating a mean absolute value of the sampled electrical signals, and wirelessly transmit the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic controller.

[0036] 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 disclosure.

[0037] The drawings described herein are only for illustrating selected embodiments rather than all possible implementations and are not intended to limit the scope of the present disclosure. [Brief description of the drawings]

[0038] [Figure 1] FIG. 1 is a diagram of a regenerative peripheral nerve interface according to certain aspects of the present disclosure. [Diagram 2] FIG. 1 is a photograph showing three free muscle grafts after removal from a subject and the surgical procedure for surgically attaching the free muscle grafts to the subject's nerve bundle, according to certain embodiments of the present disclosure. [Diagram 3] FIG. 1 is a diagram of another regenerative peripheral nerve interface and prosthetic device according to certain aspects of the present disclosure. [Figure 4] FIG. 1 is a diagram of another regenerative peripheral nerve interface according to certain aspects of the present disclosure. [Diagram 5] FIG. 1 is a block diagram of an implant device according to a particular embodiment of the present disclosure. [Figure 6] FIG. 2 is a photograph of a hardware device chip according to certain embodiments of the present disclosure. [Figure 7] FIG. 2 is a block diagram of a processing circuit with memory and communication circuitry in accordance with certain aspects of the present disclosure. [Figure 8]1 is a flowchart depicting an example control algorithm for recording amplified neural signal data, according to certain aspects of the present disclosure. [Figure 9] 1 is a flowchart depicting an example control algorithm for control of a prosthetic limb, in accordance with certain aspects of the present disclosure. [Figure 10] 1 is a flowchart depicting an example control algorithm for decoding signals for control of a prosthetic limb, in accordance with certain aspects of the present disclosure. [Figure 11] 1 is a graph showing nerve signal data corresponding to the initiation of a flexion movement. [Figure 12] 1 is a flowchart depicting an example control algorithm for monitoring nerves for pathological pain signals, in accordance with certain aspects of the present disclosure. [Figure 13] 1 is a flow chart depicting an example control algorithm for monitoring pathological bladder contraction signals in accordance with certain aspects of the present disclosure. [Figure 14] 11 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. [Figure 15] 1 is a graph showing nerve signal data corresponding to the initiation of finger flexion movement. [Figure 16] 11 is a collection of graphs showing nerve signal data from an implanted regenerative peripheral nerve interface. [Figure 17] FIG. 11 is a collection of graphs showing nerve signal data from an implanted regenerative peripheral nerve interface and from a control muscle. [Figure 18] 13 is a graph showing nerve signal data from an implanted regenerative peripheral nerve interface. [Figure 19] 1 is a graph of the percentage of predicted and actual finger flexion over time. [Figure 20] FIG. 1 is a diagram of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Figure 21]1 is a further diagram of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Figure 22] FIG. 1 is a diagram of electrical leads of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Figure 23] 13 is a further diagram of an electrical lead of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Figure 24A] FIG. 1 is a diagram of an implant device including a header for receiving an eight-contact connector according to the present disclosure. [Figure 24B] FIG. 13 is a diagram of another example of an electrical lead of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Diagram 25] 1 is a diagram of a protection circuit for an implant device according to the present disclosure. [Figure 26] FIG. 2 is a functional block diagram for processing an amplified nerve signal in accordance with the present disclosure. [Figure 27] FIG. 1 is a diagram of a prosthetic controller and a prosthetic hand according to the present disclosure. [Figure 28] FIG. 1 is a diagram of an external programmer charger and inductive charging pad according to the present disclosure. [Figure 29] FIG. 1 is a diagram of an external programmer device in communication with an implant device and a prosthetic controller according to the present disclosure. [Diagram 30] FIG. 13 is a diagram of another embodiment of a system for controlling a prosthetic device based on an amplified neural signal in accordance with the present disclosure. [Diagram 31] FIG. 13 is a diagram of an algorithm for generating prosthetic movement commands and implementing stimulation within alternating periods. [Diagram 32] FIG. 13 is a diagram of another algorithm for generating prosthetic movement commands and implementing stimulation in alternating periods while generating estimates of prosthetic movement commands during periods in which stimulation is being implemented. [Diagram 33] FIG. 13 is a diagram of an algorithm for generating prosthetic movement commands and performing stimulation while performing artifact estimation and subtraction to remove artifacts from the sensed signal caused by stimulation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings.

[0040] It should be noted that the diagrams described herein are intended to illustrate general characteristics of the methods, devices, and materials within the present disclosure for purposes of describing specific embodiments. These diagrams may not precisely reflect the characteristics of any given embodiment, and are not necessarily intended to fully define or limit specific embodiments within the scope of the present disclosure.

[0041] Example embodiments will now be described more fully with reference to the accompanying drawings, in which: 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.

[0042] In various aspects, the present disclosure provides methods for amplifying and receiving signals from a portion of a nerve, such as an individual nerve bundle, at levels greater than those produced by any conventional method or technique. Specifically, as described in more detail below, the present disclosure provides methods for amplifying and receiving signals from a portion of a nerve, such as an individual nerve bundle, at about 150 μVpp or greater, and in some instances, about 250 or 500 μVpp or greater, and up to, for example, about 1,000 μVpp or greater. As discussed above, signals detected by previous neural interface systems were typically less than 100 μVpp when recorded from within the nerve and less than 10 μVpp when recorded from a cuff around the nerve. In certain aspects, the present disclosure provides implantable neural interface devices, also referred to interchangeably as regenerative peripheral nerve interface (RPNI) devices, that facilitate amplification of signals from individual nerve bundles to about 150 μVpp or greater, and in some instances, to about 250 or 500 μVpp or greater, and up to, for example, about 1,000 μVpp or greater.

[0043] Referring to FIG. 1, a neural interface system 4 is shown within a subject or patient. The subject may be an animal with a complex nervous system, such as a mammal, such as a human, a primate, or a companion animal. A portion of a nerve 6, such as a nerve ending of the subject, may be injured or severed, such as a completely or partially damaged nerve ending caused by injury, disease, or surgery. In certain embodiments, the method may include surgically dividing, severing, cutting, and / or transecting the portion of the nerve 6 into one or more individual branches or bundles 8. It is noted that in certain variations, the method may include isolating the portion of the nerve 6 of interest to create one or more individual branches or bundles 8. The one or more individual branches or bundles 8 are each placed within a free tissue graft 10. In certain embodiments, the free tissue graft 10 may be an autograft of muscle tissue or dermal tissue previously removed from the subject. In certain preferred embodiments, the free tissue graft 10 is muscle tissue. The free tissue graft 10 is excised or resected to have a standard predetermined volume or size depending on the size of the branches or fiber bundles 8. When the free tissue graft 10 is excised, the tissue graft is devascularized and the native blood vessels are no longer functional. The predetermined volume of the free tissue graft 10 may be selected to be small enough to be properly revascularized by parallel blood flow as the free tissue graft 10 develops, while providing an area or volume large enough for the terminal branches or fiber bundles 8 of the nerve 6 to grow, as described in more detail below.

[0044] Over a period of time, such as months, the nerve bundles 8 are able to reinnervate the free tissue graft 10 and sprout nerve fibers 12 in search of new neural targets. As the free tissue graft 10 is reinnervated, action potentials from neurons traveling down the nerve then generate muscle-level signal amplitudes rather than nerve-level amplitudes. In this manner, the free tissue graft 10 (e.g., free muscle graft) acts as an amplifier of the signal generated by the terminal branches or fiber bundles 8 of the nerve 6, with signals from a single nerve bundle 8 having voltage amplitudes of about 150 μVpp or greater, and in some instances, about 250 or 500 μVpp or greater, and up to, for example, about 1,000 μVpp or greater.

[0045] The neural interface system 4 can be used with any damaged, severed, or injured portion (e.g., nerve ending) of a nerve within a subject, but is particularly suited for use with peripheral nerves. The neural interface system 4 may therefore be used for peripheral nerves that have suffered damage or injury, such as those associated with amputation surgery. Nevertheless, the methods described herein may also be used with a variety of different nerves. Thus, while in certain embodiments, the methods of the present disclosure are particularly beneficial in the case of peripheral nerves, the discussion of peripheral nerves and peripheral nerve interface devices is merely exemplary and non-limiting.

[0046] As shown in FIG. 1, the free tissue graft 10 can be configured with electrical conductors, such as electrodes 14, in electrical communication with the free tissue graft 10. The electrodes 14 are in electrical communication with wires 18a, which in turn are in electrical communication with an implant device 20, including a processing circuit 22 and an amplifier 24, as described in further detail below. In such a case, signals from the nerve bundles 8 are received by the electrodes 14 and communicated via the wires 18a, e.g., through the amplifier 24, to the processing circuit 22 of the implant device 20. Alternatively, the electrodes 14 can be omitted and the electrical conductors in electrical communication with the free tissue graft 10 can be wires 18b, which are placed directly in or on the free tissue graft 10. In such a case, signals from the nerve bundles 8 are received by the wires 18b themselves, through direct or indirect electrical communication with the free tissue graft 10, and communicated via the wires 18b, e.g., through the amplifier 24, to the processing circuit 22 of the implant device 20. As a further alternative, the electrical conductor in electrical communication with the free tissue graft 10 can be a wire lattice 17 having multiple electrode sites and multiple conductive channels disposed in or on the free tissue graft 10. The wire lattice 17 is in electrical communication with multiple wires 18c that in turn are in electrical communication with the implant device 20. In such a case, signals from the nerve bundles 8 are received by the wire lattice 17 and communicated via wires 18c, for example through an amplifier 24, to a processing circuit 22 of the implant device 20. The amplifier 24 is a high impedance amplifier. Additionally, although a single amplifier 24 is shown in FIG. 1, additional amplifiers may be further included in the implant device 20, including additional / separate amplifier circuits for one or more individual nerve bundles 8 or groups of fiber bundles 8. Additionally, multiple implant devices 20 or implant devices with additional processing circuits 22 may also be used.As referred to herein, the neural interface system 4 may be an implantable neural interface device, or RPNI device, and generally includes an implant device 20 including a free tissue graft 10, associated wires 18a, 18b, 18c, an electrode 14 or a wire grid 17 including multiple electrodes, if any, and a processing circuit 22.

[0047] For example, implant device 20 may be a medical device implantable within a subject that is similar to an automatic cardiac defibrillator, but has the processing capability to receive, process, record, and / or communicate neural signals received from free tissue graft 10, as described herein. Because signals from individual nerve bundles 8 are amplified by free tissue graft 10 (e.g., free muscle graft), e.g., to a level of about 150 μVpp or greater, the electronics contained within implant device 20 are smaller, less expensive, require less processing capability, and / or consume less battery power than the electronics that would be required to adequately and meaningfully receive, record, and process neural signals detected by previous systems, which, as discussed above, are typically less than 100 μVpp when received from within the nerve and less than 10 μVpp when received from a cuff around the nerve. Furthermore, because the signals from the individual nerve bundles 8 are amplified by the free tissue graft 10, e.g., to a level of about 150 μVpp or greater, the signals are less susceptible to noise and interference, have a higher signal-to-noise ratio, and are more representative of and consistent with the actual neural signals produced by the individual nerve bundles 8. For example, the signals can have a signal-to-noise ratio of 4 or greater. In particular, electrical signals of such levels may be produced by an implantable neural interface system 4 that consists essentially of the free tissue graft 10, and one or more electrical conductors (e.g., wires 18a, 18b, 18c, electrodes 14, and / or wire grid 17), in certain embodiments, along with one or more portions of the regenerated and reinnervated nerves 6 in the free tissue graft.

[0048] In certain aspects, a method of amplifying a nerve signal in a subject includes placing a portion of a nerve 6 (e.g., nerve bundle 8) in a free tissue graft 10 and fixing the portion of the nerve 6 (nerve bundle 8) within the free tissue graft 10. For example, the free tissue graft 10 can be affixed to the nerve bundle 8 via suturing, glue, tension, or other suitable attachment methods or mechanisms. At least one electrical conductor (e.g., electrode 14, wires 18a, 18b, 18c, and / or wire grid 17) may then be introduced into the free tissue graft 10. It should be noted that at least one electrical conductor may be introduced into the free tissue graft prior to fixing a 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 may have a maximum thickness of about 5 mm or less. One or more portions of the nerve 6 (nerve bundle 8) are thus regenerated within the free tissue graft, thus reinnervating the tissue. Such nerve regeneration can include growing sprouting nerve fibers 12. In this manner, the nerve 6 is thus capable of generating an amplified electrical signal of about 150 microvolts or more without any external electrical input, as previously discussed above. In particular, the ability to amplify and generate electrical signals from the nerve reflects the voluntary spontaneous generation of electrical signals from the subject at high voltage levels not previously possible. Such voluntary spontaneous electrical signals (e.g., naturally generated from a motor nerve) can be distinguished from stimulating nerve signals (e.g., stimulation with combined compound action potentials (CMAPs) resulting from external nerve activation) generated by introducing external electrical input to the nerve for activation.

[0049] In other particular aspects, the method can include cutting a portion of a nerve in the subject, such as cutting the end of the nerve, to create one or more branches or bundles. In particular aspects, the cutting can include cutting the nerve end into multiple portions, such as branches / bundles. Thus, the placement of the nerve within the free tissue graft and the introduction of the conductor into the free tissue graft mass can be repeated for each portion of the nerve. The method can further include removing the free tissue graft from the subject's tissue prior to placement of the cut ends. In particular aspects, the tissue is muscle tissue. In alternative aspects, the tissue can be dermis tissue. As discussed in more detail below, in particular aspects, the maximum dimension of the free tissue graft is about 10 cm or less. In other aspects, the maximum dimension of the free tissue graft is about 5 cm or less.

[0050] In certain embodiments, methods according to certain embodiments of the present disclosure can further include stimulating one or more portions (e.g., branches / bundles) of the nerve with a stimulation signal delivered through one or more electrical conductors in electrical communication with the free tissue graft, providing the ability to deliver sensory feedback via the stimulation to the subject's brain via the neural interface system 4.

[0051] Referring to FIG. 2, three free tissue grafts 10 of free muscle tissue are shown after removal from a subject, but prior to surgical attachment to the subject's nerve bundles. FIG. 2 further includes a photograph 28 illustrating a surgical procedure for surgically attaching the free tissue grafts 10 to the subject's nerve bundles. Further description of surgically attaching free tissue grafts 10, such as muscle grafts, also referred to as autografts, a freely grafted piece of autologous muscle tissue, to nerve bundles is provided in commonly assigned U.S. Pat. No. 6,393,326, published Nov. 14, 2013, the entire disclosure of which is incorporated herein by reference.

[0052] Since the free tissue graft 10, e.g., a muscle graft, may be surgically removed from a non-vital donor muscle in a subject, the free tissue graft 10 undergoes a complete denervation process after removal, thus terminating any previously existing neural development in the free tissue graft 10. As discussed above, this removal process also causes devascularization of the native cells of the free tissue graft 10. When the free tissue graft 10 is surgically affixed to the nerve bundle 8, the free tissue graft 10 undergoes a process of reinnervation, thus the affixed nerve bundle 8 reinnervates the free tissue graft 10 and sprouts nerve fibers 12, which grow within the free tissue graft 10 in search of new neural targets. Having previously undergone a process of denervation, signals from the newly affixed nerve bundle 8 and the newly sprouted nerve fibers 12 do not have to compete with the remaining neural signals from the nerve bundles and nerve fibers that previously innervated the free tissue graft 10.

[0053] Furthermore, when surgically re-attached to the subject, rather than simply dying and being reabsorbed by the subject's body, the free tissue graft 10 can obtain nutrients through the process of uptake. Thus, even without a native vascular blood supply, if the free tissue graft 10 is within an optimal volume / size range, the free tissue graft 10 can absorb nutrients and blood through the surrounding tissues and fluids to support the process of nerve regeneration. Eventually, as the free tissue graft 10 reintegrates with the subject's body, a new blood supply network can be established. This process of denervation of the free tissue graft 10 prior to reinnervation of the free tissue graft 10 by the attached nerve bundles through newly sprouted nerve fibers 12, coupled with the process of uptake and revascularization, results in an area of ​​muscle or other tissue where highly specific electrical signals from individual nerve bundles 8, of approximately 150 μVpp or more, can be received, for example, by the implant device 20.

[0054] As mentioned above, to facilitate the process of nerve regeneration and inhalation, the free tissue graft 10 is preferably within an optimal volume / size range. For example, the volume / size of the free tissue graft 10 may be selected to be small enough to be quickly revascularized by parallel blood flow while providing an area or volume large enough for nerves to grow without forming unorganized neuromas. The maximum dimension of the free tissue graft 10 may be about 10 cm or less in certain preferred embodiments. For example, in certain variations, the free tissue graft 10 may have a maximum dimension in any direction of about 10 cm or less. For example, in certain variations, the length of the free tissue graft 10 may be about 10 cm or less, or more preferably about 5 cm or less. Furthermore, the width of the free tissue graft 10 may be about 10 cm or less, or more preferably about 5 cm or less. The thickness of the free tissue graft 10 may optionally be about 2 to 3 cm or less. Furthermore, the optimal dimensions of the free tissue graft 10 may include a length of about 5 cm or less, and a diameter of about 2 cm or more to about 3 cm or less. For example, optimal preferred dimensions for free tissue graft 10 may include a length of approximately 3.5 cm and a diameter of approximately 2 cm. It should be noted that free tissue graft 10 may have a variety of entirely different sizes and / or geometries, and those described herein are exemplary. Additionally, a discussion of the dimensions of a piece of autologous muscle tissue to be transplanted free from a subject is contained, for example, in paragraphs

[0082] through

[0088] of U.S. Patent Publication 2013 / 0133633, published Nov. 14, 2013, which is incorporated herein by reference in its entirety.

[0055] 3, an example embodiment 100 shows a plurality of free tissue grafts 10 (e.g., free muscle grafts) and electrodes 14 connected to an implant device 20. In addition to receiving signals from individual nerve bundles through the free tissue grafts 10 and electrodes 14, the implant device also controls an end device, in this case a prosthetic hand 110. Specifically, as shown in FIG. 3, the radial nerve 102, median nerve 104, and ulnar nerve 106 are each separated into a plurality of individual nerve bundles that are affixed to corresponding free tissue grafts 10 and in electrical communication with the processing device 22 of the implant device 20 through electrical communication with the electrodes 14.

[0056] As discussed in more detail below, the processing circuitry 22 of the implant device 20 monitors signals from the various fiber bundles and controls, for example, flexion and extension of the prosthetic hand 110 based on an analysis of the received signals. For example, as discussed in more detail below, training data can be obtained through a calibration process, so that the subject is asked to perform certain movements while neural signals are monitored and recorded by the processing circuitry 22 of the implant device 20 and communicated to an external computing device, such as a desktop computer or laptop. The training data set is then analyzed and used to estimate parameters used by the processing circuitry 22 to move the prosthetic hand 110, which are then downloaded from the external computing device to the processing circuitry 22 of the implant device 20. For example, as shown in FIG. 3, the implant device 20 is in communication with actuators 112 that flex and extend individual fingers of the prosthetic hand 110, as discussed in more detail below.

[0057] 4, in addition to sensing or reading signals generated by nerve bundles 8 that are amplified through the free tissue graft 10, the RPNI device of the present disclosure can also be used to stimulate individual nerve bundles 8 or individual nerve fibers. For example, as shown in FIG. 4, the free tissue graft 10 is configured with three electrodes 30a, 30b, 30c that communicate with the processing circuit 22 of the implant device 20 through amplifiers 32, 32b, 32c and wires 34a, 34b, 34c. In this manner, the processing circuit 22 can stimulate the individual nerve bundles 8 using, for example, a negative voltage stimulation signal or a positive voltage inhibitory stimulation signal, as discussed in more detail below. In general, a negative voltage signal causes the nerve to fire, while a positive voltage inhibits the nerve from firing.

[0058] Existing clinical applications such as vagus nerve stimulation typically use a cuff around the entire nerve. Thus, a large portion of the nerve is usually stimulated. With an RPNI device such as that shown in FIG. 4, the processing circuit 22 can still address stimulation to a specific fiber bundle by directing signals to the electrodes 30a, 30b, 30c through the wires 34a, 34b, 34c. More specifically, with the free tissue graft 10, it is possible to allow a single fiber bundle 8, which may be approximately 1 mm in diameter, to expand into a 1 cm×3.5 cm structure for stimulation. Stimulating the entire structure, i.e., the entire free tissue graft 10, can specifically address a single corresponding fiber bundle 8.

[0059] Furthermore, through the use of multiple electrical contacts, such as multiple electrodes, current steering can be used to enable stimulation of even more specific areas, such as subsections of fiber bundles 8, or individual fibers. For example, when utilizing only a single negative contact for stimulation, the negative voltage may be diffused and dissipated. By using current steering, on the one hand, the negative voltage can be surrounded by the positive voltage to focus the negative voltage at a single location. With continued reference to FIG. 4, a negative voltage may be applied to electrode 30b, while a positive voltage may be applied to electrodes 30a and 30c to focus more on the location for application of the negative voltage from electrode 30b.

[0060] As discussed in more detail below, neurostimulation can be used, for example, for a sensory prosthesis to stimulate nerves in response to pressure sensed by a pressure sensor in a prosthesis. Additionally, neurostimulation can be used to inhibit pathological pain signals. Additionally, neurostimulation can be used to inhibit pathological contractions of the bladder, for example. Additionally, neurostimulation 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, kidneys, and the like. For example, such neurostimulation may be used on the renal arteries to disrupt and treat abnormal nerve signals in the kidneys that may otherwise cause high blood pressure.

[0061] With reference to FIG. 5, further details regarding the implant device 20 are shown. As discussed above, the implant device 20 can receive an amplified nerve signal input 52 from the free tissue graft 10 via wires 18a, 18b, 18c (shown in FIG. 1). As further discussed above, the implant device 20 can communicate a nerve stimulation signal output 54 to stimulate nerves at electrodes 30a, 30b, 30c via wires 34a, 34b, 34c (shown in FIG. 4). As further discussed above, the implant device 20 can communicate a prosthetic control signal output 56 to control the movement of a prosthetic device. For example, the implant device 20 can communicate the prosthetic control signal output 56 via a data bus, such as a controller area network (CAN) bus, to control the flexion and extension of a prosthetic hand 110 (shown in FIG. 3). For example, implant device 20 can communicate a prosthetic control signal output 56 to control individual actuators 112 (shown in FIG. 3) of a prosthetic limb. Additionally, as discussed further above, implant device 20 can receive a prosthetic sensor input signal 58 generated by one or more pressure sensors, for example, corresponding to pressures sensed by the pressure sensors of the prosthetic limb. As discussed above, implant device 20 can generate a neural stimulation signal output 54 based on the pressure sensor signal input received by the implant device from the pressure sensors of the prosthetic limb.

[0062] As shown in FIG. 5, the implant device includes the processing circuit 22, as well as a communication circuit 50 and a memory 62 that both communicate with the processing circuit 22. The communication circuit 50 enables the implant device, and specifically the processing circuit 22 of the implant device 20, to communicate wirelessly to a computing device external to the implant device 20, including, for example, a computing device external to the subject, such as a desktop or laptop computer. In this manner, the processing circuit 22 can communicate data, such as amplified neural signal input data received via the amplified neural signal input 52. Such communication can be used during a calibration process to receive training and calibration data from the implant device 20 at the external computing device for review and analysis, as well as to communicate estimated operating parameters and configuration data used by the implant device 20, for example, to move a prosthetic limb, or to generate a neural stimulation signal output. The communication circuit 50 can include an antenna and a receiver and transmitter, or transceiver, for communicating via wireless radio frequency (RF) 60. For example, the communications circuitry 50 can communicate via a wireless protocol, such as the CEN ISO / IEEE 11073 communications protocol for communication between medical devices and external information systems, or alternatively, the communications circuitry 50 can communicate via other wireless protocols, such as WiFi® or Bluetooth®.

[0063] Memory 62 may be used by processing circuitry 22 to store amplified neural signal input data received via amplified neural signal input 52, for example, prior to communication to an external computing device via communications circuitry 50. Memory 62 may also be used to store estimated operating parameters and configuration data received from the external computing device and used by the implant device during operation. Memory 62 may also be used by processing circuitry 22 to store event or operating history data, or any other data associated with the various inputs and outputs received or generated by processing circuitry 22.

[0064] Referring to FIG. 6, a hardware device chip 64 is shown that can include or be used to implement, for example, the processing circuit 22, the communication circuit 50, and the memory 62 of the implant device (shown in FIG. 5). The hardware device chip 64 can also include or be used to implement the amplifier 24 (shown in FIG. 1) and the amplifiers 32a, 32b, 32c (shown in FIG. 4). The hardware device chip 64 can preferably include a high input impedance bio-amplifier configured to process the high output impedance biological signal received from the free tissue graft 10. Furthermore, the hardware device chip 64 can preferably include functionality to reject large common mode signals, such as specifically recording from a pair of electrodes, to correlate the received signal with a local reference near the RPNI device, or to use a strong high pass filter in the first stage. The hardware device chip 64 can preferably include an amplifier to amplify the signal received from the nerve bundle 8, for example, by 1,000 times. Preferably, the hardware device chip 64 has very low noise, although this feature may be less important considering the relatively high amplitude of the received amplified nerve signal input from the nerve bundle 8. Additionally, the hardware device chip 64 may preferably include a band pass filter for filtering the received amplified nerve signal input between 10,000 and 2,000 Hz before further processing. Additionally, the hardware device chip 64 may preferably be capable of taking the absolute value in the analog domain of the received amplified nerve signal input.

[0065] 7, further details are shown regarding the processing circuit 22, which is shown in communication with the communication circuit 50 and memory 62 of the implant device 20. The processing circuit 22 includes a neural input signal conditioning circuit 70 for receiving and conditioning the amplified neural signal input 52 received from the nerve bundle through the free tissue graft 10. The function and operation of the neural input signal conditioning circuit 70 is discussed in further detail below. The processing circuit 22 also includes a neural input signal decode circuit 72 for processing and decoding the signal data after conditioning by the neural input signal conditioning circuit 70. The function and operation of the neural input signal decode circuit 72 is discussed in further detail below. The processing circuit 22 also includes a neural stimulation signal output circuit 74 for generating the neural stimulation signal output 54. The function and operation of the neural stimulation signal output circuit 74 is discussed in further detail below. The processing circuit 22 also includes a prosthetic device control circuit 76 for generating a prosthetic device control signal output 56 for controlling a prosthetic limb. The function and operation of the prosthetic control circuit 76 is discussed in further detail below. The prosthetic control signal output 56 for controlling the prosthetic may be communicated to the prosthetic via a data bus such as a CAN bus. The processing circuit 22 also includes a prosthetic sensor receiver circuit 78 for receiving a prosthetic sensor signal input 58 from a pressure sensor in the prosthetic. The function and operation of the prosthetic sensor receiver circuit 78 is discussed in further detail below.

[0066] 8, there is shown a control algorithm 800 for receiving and recording amplified neural signal data from a free tissue graft 10. The control algorithm 800 may be implemented by the processing circuitry 22 of the implant device 20. More particularly, the control algorithm 800 may be implemented at least in part by the neural input signal conditioning circuitry 70 (shown in FIG. 7) of the processing circuitry 22. The control algorithm 800 begins at 802.

[0067] At 804, the neural input signal conditioning circuit 70 of the processing circuit 22 receives the amplified neural signal from a conductor in electrical communication with the free muscle graft. As described in detail above with reference to FIG. 1, the conductor may be an electrode 14, wire grid 17, or wire 18b in electrical communication with the free tissue graft 10. As further described above, the electrical signal can have a voltage amplitude of about 150 μVpp or more, and in some instances about 250 or 500 μVpp or more, and up to, for example, about 1,000 μVpp or more. Furthermore, in embodiments where an amplifier 24 (shown in FIG. 1) is used, the signal may be amplified before being received by the processing circuit 22. In such a case, for example, the signal may be preconditioned by being amplified by 1,000 times in the amplifier 24 before being received by the neural input signal conditioning circuit 70 of the processing circuit 22.

[0068] At 806, the neural input signal conditioning circuit 70 of the processing circuit 22 conditions and extracts features from the received signal from the free tissue graft 10. For example, in an embodiment that does not include an amplifier 24 (shown in FIG. 1), the neural input signal conditioning circuit 70 may amplify the received signal, for example, by 1,000 times. The neural input signal conditioning circuit 70 may then filter the received signal using a predetermined analog frequency range. For example, the predetermined analog frequency range may be between 10 Hz and 1,000 Hz, or between 10 Hz and 2,000 Hz. After filtering, the neural input signal conditioning circuit 70 may digitize the filtered signal using a predetermined sampling rate. For example, the neural input signal conditioning circuit 70 may digitize the filtered signal using a sampling rate of 30,000 samples per second. The digitized signal may then be digitally filtered using a predetermined digital frequency range. For example, the predetermined digital frequency range may be from 100 to 500 Hz. After digital filtering, the signal may then be down-sampled to 1,000 samples per second. Due to the relatively large voltage amplitude of the original received signal, the conditioning and feature extraction performed in step 806 results in a robust and well-defined signal that is less susceptible to noise or interference when compared to systems that do not utilize free tissue grafts 10 to amplify the signal from the nerve bundle.

[0069] At 808, the processing circuitry 22 records the resulting signal data in memory 62 and / or communicates the resulting signal data to an external computing device using the communications circuitry 50. For example, the resulting signal data can be stored in memory 62 of the implant device 20 and then communicated via a batch communications process to the external computing device through the communications circuitry 50. Alternatively, the memory 62 can act as a buffer to receive and store the resulting signal data for further processing by the processing circuitry 22 or for communication to the external computing device through the communications circuitry 50. Alternatively, the resulting signal data can be streamed in real time to the external computing device through the communications circuitry 50.

[0070] After recording or communicating the resulting signal data at 808, processing circuitry 22 loops back to 804 and continues to receive the amplified nerve signal. Although control algorithm 800 is shown as sequential steps for purposes of illustration, it will be understood that the individual steps can be continually occurring in parallel by processing circuitry 22 as the amplified nerve signal is continually received in real time.

[0071] 9, a control algorithm 900 for controlling a prosthetic limb, such as the prosthetic hand 110 (shown in FIG. 3) based on signals received from a free tissue graft 10 is shown. The control algorithm 900 may be implemented by the processing circuitry 22 of the implant device 20. More specifically, the control algorithm 900 may be implemented at least in part by the neural input signal conditioning circuitry 70, the neural input signal decoding circuitry 72, and the prosthetic control circuitry 76 (shown in FIG. 7) of the processing circuitry 22. The control algorithm 900 begins at 902.

[0072] At 904, neural input signal conditioning circuit 70 of processing circuit 22 receives the amplified neural signal from a conductor in electrical communication with the free muscle graft. The functionality of step 904 was described above with respect to step 804 of FIG. 8 and will not be repeated here.

[0073] At 906, the neural input signal conditioning circuitry 70 of the processing circuitry 22 conditions and extracts features from the received signal from the free tissue graft 10. The functionality of step 906 is described above with respect to step 806 of FIG. 8 and will not be repeated here.

[0074] At 908, the neural input signal decoding circuit 72 decodes the resultant signal data to determine whether the resultant signal data corresponds to, for example, flexion or extension of the prosthetic limb. Although the control algorithm 900 of FIG. 9 is described in terms of decoding the resultant signal data for a flexion or extension action, it will be understood that other prosthetic control motions may be similarly decoded from the resultant signal data, as desired. The neural input signal decoding circuit 72 may use a 1 / 2 classifier, such as a Naive Bayes classifier, or regression analysis to determine whether the resultant signal data over a portion of a predetermined period, such as 25 milliseconds (ms), indicates flexion or extension. Further details regarding decoding the resultant signal data are described below with respect to a control algorithm 1000 for decoding signals for control of a prosthetic limb, shown in FIG. 10.

[0075] At 910, processing circuitry 22 determines whether the resulting signal data for a portion of the predetermined time period corresponds to flexion or extension of the prosthesis. When the resulting signal data at 910 corresponds to extension, processing circuitry proceeds to 912 and prosthetic device control circuitry 76 of processing circuitry 22 moves the prosthesis in an extension direction. When the resulting signal data at 910 corresponds to extension, processing circuitry proceeds to 914 and prosthetic device control circuitry 76 of processing circuitry 22 moves the prosthesis in a flexion direction. For example, in the case of a prosthetic hand 110 as shown in FIG. 3, processing circuitry 22 can move actuator 112 of prosthetic hand 110 in a flexion or extension direction, as appropriate. After moving the prosthetic hand at steps 912 or 914, processing circuitry 22 loops back to 904.

[0076] Referring to Figure 10, a control algorithm 1000 for decoding signals for control of a prosthetic limb is shown. The control algorithm 1000 may be implemented by the processing circuitry 22 of the implant device 20. More particularly, the control algorithm 1000 may be implemented at least in part by the neural input signal decoding circuitry 72. The functionality of the control algorithm 1000 shown in Figure 10 is subsumed in step 908 of Figure 9. The control algorithm 1000 begins at 1002.

[0077] At 1004, the neural input signal decode circuit 72 determines whether the current sample group for the portion of the predetermined time period is complete. For example, the portion of the predetermined time period may be 25 ms and the sample interval may be 1 ms apart. In such a case, the neural input signal decode circuit 72 may wait at step 1004 until a full sample group of 25 samples at 1 ms intervals is completed. When the sample group is not yet complete, the neural input signal decode circuit 72 loops back to 1004. When the sample group is complete, the neural input signal decode circuit 72 proceeds to 1006.

[0078] At 1006, the neural input signal decoding circuit 72 classifies each sample in the sample group using a 1 / 2 classifier. For example, when the neural input signal decoding circuit 72 is decoding the resulting signal data as to whether it is a flexion or extension, the neural input signal decoding circuit 72 may classify each sample in the sample group as a flexion sample or an extension sample. For example, the neural input signal decoding circuit 72 may classify each sample in the sample group as a flexion sample or an extension sample using a 1 / 2 Naive Bayes classifier, or regression analysis.

[0079] The 1 / 2 Naive Bayes classifier can use training data previously collected from the subject during calibration procedures and routines. For example, the subject may be instructed to perform a flexion or extension action, and the resulting neural signal data can be recorded by the processing circuitry 22 and communicated to an external computing device for analysis. Based on the collected training data, a Gaussian distribution can be estimated or calculated based on the received neural signal data for each of the flexion and extension movements. For example, the Gaussian distributions for the flexion and extension movements will therefore have different mean values ​​and variances. Parameters and data for the 1 / 2 Naive Bayes classifier can be estimated by the external computing device based on the collected training data, and then communicated to the processing circuitry 22 and stored in memory 62 for use by the neural input signal decoding circuitry 72 in decoding the neural signal data.

[0080] During step 1006, the neural input signal decode circuit 72 can compare each sample in the sample group to a previously determined Gaussian distribution having different mean values ​​and variances for flexion and extension movements, and calculate the probability that the particular sample was derived from each of the two distributions. Each sample is then classified based on which of the two movements has a higher probability for the particular sample. For example, if a particular sample has a higher probability of corresponding to a flexion movement, the sample is classified as a flexion sample. If a particular sample has a higher probability of corresponding to an extension movement, the sample is classified as an extension sample. Once all of the samples in the sample group have been classified, the neural input signal decode circuit 72 proceeds to 1008.

[0081] At 1008, the neural input signal decode circuit 72 determines whether there are more flexion samples or more extension samples in the particular sample group and classifies the entire sample group based on the determination. For example, when there are more flexion samples in the sample group, the sample group is classified as a flexion sample group, and when there are more extension samples in the sample group, the sample group is classified as an extension sample group. In this manner, the neural input signal decode circuit 72 predicts, for example, whether a group of samples in a particular sample group indicates a flexion or extension motion. It is understood that other motions may be included in the classification and prediction process as well. After classifying the sample group, the neural input signal decode circuit 72 loops back to 1004.

[0082] Thus, with reference to both FIG. 9 and FIG. 10, processing circuitry 22 can determine or predict whether the neural signal data during a portion of a particular predetermined period, such as 25 ms, corresponds to or indicates a flexion or extension motion. Further, prosthetic device control circuitry 76 can send a control command to the prosthesis every 25 ms based on the classification of the current or most recent sample group. In this manner, processing circuitry 22 can continually monitor and decode the neural signal data and send corresponding commands to operate the prosthesis. Although the above examples are described using a portion of a predetermined period, shorter or longer portions of the predetermined period can be used.

[0083] 11, an example graph of nerve signal data over a 5 second period corresponding to the onset of a flexion movement is shown. Similarly, with reference to FIG. 15, an example graph of nerve signal data over a 400 millisecond period corresponding to the onset of a finger flexion movement is shown.

[0084] As described above with respect to Figures 9 and 10, training data collected from a subject during a calibration routine can be used to generate a mapping of, for example, received nerve signals to corresponding prosthetic movements or actions. In the example of a person with a transradial resection, the individual nerve bundles may be well mapped to individual hand muscles simulated in the prosthesis. In the example of a person with a humeral resection, the training data can be used to determine which nerve bundles or sets of nerve signal inputs best correlate to which individual hand muscles simulated in the prosthesis.

[0085] Additionally, neural signal data such as average signal power, number of zero-crossing events, or counts of detected spikes can be monitored, recorded, and analyzed for each signal from each nerve bundle and used to calculate the desired velocity, for example, for all five fingers of a prosthetic hand, for a single command to be sent to the prosthetic hand every time step, for example, every 25 ms. There is no one-to-one correspondence between specific muscles and the velocities of individual fingers. For example, to bend only the pinky finger, the subject may need to simultaneously extend the index finger. Thus, finger velocities can be traced back to muscle activity across all of the neural signal channels to determine a consistent global map. Various algorithms are available to estimate instantaneous velocities from various signals, including, for example, linear filters, Kalman filters, and particle filters.

[0086] Additionally, individual distinct states, such as grasping and pointing, can be predicted using linear discriminants, Naive Bayes classifiers, or support vector machines. In each case, a training data set is obtained through a calibration process by asking the subject to perform various movements or actions and using the implant 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 motion parameters used by the processing circuitry 22 and, for example, the prosthetic control circuitry 76 to control the prosthetic hand. The estimated motion parameters can then be downloaded to the processing circuitry 22 through the communications circuitry 50 and stored in memory 62 for use by the processing circuitry 22, for example, to provide real-time estimation of finger velocities to move the prosthetic hand.

[0087] 12, a control algorithm 1200 for monitoring nerves for pathological pain signals is shown. The control algorithm 1200 may be implemented by the processing circuitry 22 of the implant device. More specifically, the control algorithm 1200 may be implemented 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.

[0088] At 1204, neural input signal conditioning circuit 70 of processing circuit 22 receives the amplified neural signal from a conductor in electrical communication with the free muscle graft. The functionality of step 1204 was described above with respect to step 804 of FIG. 8 and will not be repeated here.

[0089] In 1206, the neural input signal conditioning circuitry 70 of the processing circuitry 22 conditions and extracts features from the received signal from the free tissue graft 10. The functionality of step 1206 was described above with respect to step 806 of FIG. 8 and will not be repeated here.

[0090] At 1208, the neural input signal decoding circuit 72 decodes the resulting signal data to determine whether the resulting signal data is indicative of, for example, a pathological pain signal. The decoding performed at 1208 is similar to the decoding described above with respect to step 908 of FIG. 9 and steps 1004 through 1008 of FIG. 10, which described decoding the resulting signal data to determine whether the resulting signal data is indicative of a flexion or extension action. As with the decoding described above with respect to FIGS. 9 and 10, the decoding performed at 1208 may similarly use a 1 / 2 classifier, such as a Naive Bayes classifier, based on a training data set collected during a calibration procedure on the subject to determine whether the resulting signal data corresponds to a condition in which a pathological pain signal is being generated. After decoding the resulting signal at 1208, the processing circuit 22 proceeds to 1210.

[0091] At 1210, processing circuit 22 determines whether a pathological pain signal was detected based on the decoding of the resulting signal data. When a pathological pain signal is detected, processing circuit 22 proceeds to 1212 and stimulates the appropriate nerve bundle with an inhibitory stimulus. Specifically, 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 inhibit or reduce pathological pain signal activity in the nerve bundle. In this manner, pain signals in the subject can be alleviated without permanently losing sensation in the particular nerve or nerve bundle at issue. After stimulating the nerve with an inhibitory stimulus at 1212, or after determining at 1210 that a pathological pain signal was not detected, processing circuit 22 loops back to 1204.

[0092] 13, a control algorithm 1300 for monitoring pathological bladder contraction signals is shown. The control algorithm 1300 may be implemented by the processing circuitry 22 of the implant device. More specifically, the control algorithm 1300 may be implemented 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.

[0093] At 1304, neural input signal conditioning circuit 70 of processing circuit 22 receives the amplified neural signal from a conductor in electrical communication with the free muscle graft. The functionality of step 1304 was described above with respect to step 804 of FIG. 8 and will not be repeated here.

[0094] In 1306, the neural input signal conditioning circuitry 70 of the processing circuitry 22 conditions and extracts features from the received signal from the free tissue graft 10. The functionality of step 1306 was described above with respect to step 806 of FIG. 8 and will not be repeated here.

[0095] At 1308, the neural input signal decoding circuit 72 decodes the resulting signal data to determine whether the resulting signal data is indicative of, for example, a pathological bladder contraction signal. The decoding performed at 1308 is similar to the decoding described above with respect to step 908 of FIG. 9 and steps 1004 through 1008 of FIG. 10, which described decoding the resulting signal data to determine whether the resulting signal data is indicative of a flexion or extension action. As with the decoding described above with respect to FIG. 9 and FIG. 10, the decoding performed at 1308 may similarly use a 1 / 2 classifier, such as a Naive Bayes classifier, based on a training data set collected during a calibration procedure on the subject to determine whether the resulting signal data corresponds to a condition in which a pathological bladder contraction signal is being generated. After decoding the resulting signal at 1308, the processing circuit 22 proceeds to 1310.

[0096] At 1310, processing circuit 22 determines whether a pathological bladder contraction signal was detected based on the decoding of the resulting signal data. When a pathological bladder contraction signal is detected, processing circuit 22 proceeds to 1312 and stimulates the appropriate nerve bundle with an inhibitory stimulus. Specifically, nerve stimulation signal output circuit 74 of processing circuit 22 can stimulate the appropriate nerve bundle with a positive voltage to inhibit nerve activity, inhibiting or reducing pathological bladder contraction signal activity in the nerve bundle. After stimulating the nerve with an inhibitory stimulus at 1312, or after determining at 1310 that a pathological pain signal was not detected, processing circuit 22 loops back to 1304.

[0097] Although described in the context of monitoring and suppressing pathological bladder contraction signals, the control algorithm 1300 described with respect to FIG. 13 can be similarly adapted for other applications. For example, the control algorithm 1300 may be suitably adapted for sphincter control or erectile dysfunction. Similarly, the control algorithm 1300 may be adapted to control nerves associated with internal organs, such as the liver, adrenal glands, stomach, pancreas, and kidneys. In each case, training data is collected and analyzed from the subject to generate appropriate monitoring parameters, which are then downloaded to the implant device 20. The implant device 20 then monitors nerve signal activity to determine whether neural stimulation is appropriate.

[0098] 14, a control algorithm 1400 for stimulating nerves based on a sensed pressure signal from a prosthetic device is shown. For example, a prosthetic device, such as the prosthetic hand 110 shown in FIG. 3, may be equipped with a pressure sensor, for example, located at a fingertip of the prosthetic. The pressure sensor may sense pressure and communicate a pressure signal back to the processing circuit 22 of the implant device 20. The control algorithm 1400 may be implemented by the processing circuit 22 of the implant device. More specifically, the control algorithm 1400 may be implemented at least in part by the prosthetic sensor receiver circuit 78 and the nerve stimulation signal output circuit 74. The control algorithm 1400 begins at 1402.

[0099] At 1404, the prosthetic sensor receiver circuit 78 receives a pressure signal from the prosthetic pressure sensor corresponding to the pressure sensed at the pressure sensor location. At 1406, the nerve stimulation signal output circuit 74 stimulates individual nerve bundles based on the received sensed pressure signal. A calibration procedure on the subject can be used to generate training data to determine which individual nerve bundles should be best mapped to which pressure sensors. Furthermore, the firing rate or level of stimulation can be matched to the level of pressure sensed by the pressure sensor. In this manner, the implant device can communicate tactile feedback signals from the prosthetic to the appropriate nerve bundles.

[0100] The specific examples which follow are provided for the purpose of illustration of how to make and use the compositions, devices, and methods of the present technology, and are not intended to represent that a given embodiment of the present technology has or has not been performed, or has or has not been tested, unless expressly stated otherwise.

[0101] "Example 1" Non-human primate RPNI studies In the following example, two non-human primates, Monkey R and Monkey L, were surgically implanted with RPNIs in the forearms. Specifically, Monkey R was implanted with three RPNIs and Monkey L was implanted with four RPNIs. Muscle grafts were attached to small branches of the median and radial nerves that provided independent finger flexion / extension and thumb flexion signals. Surgery followed a standard surgical procedure checklist and animals were monitored daily in their cages for 10 days post-operatively and then observed in a primate chair during daily experiments thereafter.

[0102] No major problems were noted, and the animals regained normal use of their limbs within one week after surgery. At the second surgery in both animals, obvious revascularization was observed in the muscle grafts. In response to electrical stimulation, RPNI produced large amplitude compound muscle action potentials (CMAPs), indicating reinnervation of the muscle grafts by the implanted nerve bundles.

[0103] In monkey L's third surgery, four bipolar "IM-MES" intramuscular electrodes manufactured by Ardiem Medical were implanted into two aged RPNIs for comparison and into a healthy intact muscle (ECRB, wrist extensor). One electrode was placed into the muscle graft of the newly created RPNI construct, which was then aged for three months to produce a high amplitude signal. The presence of the electrodes during the aging phase had no negative adverse effect on RPNI regeneration, reinnervation, and aging. The electrode leads were tunneled subcutaneously from the monkey's forearm to the back and exited through the skin at the back for connection to recording equipment. Daily recordings from these implanted electrodes were taken during task behavior. The percutaneous sites where the leads exited were lightly cleaned with betadine solution weekly and no infection was noted. The sites appeared clean with minimal inflammation and did not cause any obvious distress to the animals.

[0104] 16, graph 1600 shows the voluntary RPNI signal in μV recorded by a semi-chronic Ardiem IM-MES electrode in monkey L, along with the calculated percentage of flexion corresponding to the RPNI signal. Graph 1602 shows the voluntary RPNI signal in μV recorded by a percutaneous thin wire electrode in monkey L, along with the calculated percentage of flexion corresponding to the RPNI signal. Graph 1604 shows the voluntary RPNI signal in μV recorded by a percutaneous thin wire electrode in monkey R, along with the calculated percentage of flexion corresponding to the RPNI signal.

[0105] Referring to graph 1600, signals recorded by IM-MES electrodes vary between animals and RPNI implants, with amplitudes ranging from 50 to 500 μVpp. Graphs 1600, 1602, 1604 show representative signals that appear similar to sparse electromyogram (EMG) signals, usually displaying multiple apparent single motor units. The right-most portion of graph 1600 shows a zoomed-in portion of the voltage signal indicative of an individual muscle contraction. All putative single units observed correspond reliably to flexion events, are approximately 4 ms in length, and have variable firing rates. The high signal-to-noise ratio (SNR) of the RPNI signal enabled automated detection of voluntary RPNI activation with 95+% accuracy using a linear discriminant classifier. The RPNI signal is used to control the prosthetic hand in real time while monkey L is performing a behavioral task.

[0106] "Example 2" Human RPNI studies In the following example, three RPNIs were surgically implanted in a human for neuroma control. The patient underwent a distal transradial amputation just proximal to the wrist. Approximately 1 x 3 cm muscle grafts were taken from the surrounding tissue and sutured separately to the distal ends of the median, ulnar, and radial nerves. At this level, the radial nerve (and therefore the RPNI graft) contains only sensory fibers that originally innervated the dorsal skin of the hand. The median and ulnar nerves and the RPNI contain a mixture of sensory and motor fibers to the hand that originally innervated the intrinsic muscles of the hand. Electromyographic (EMG) activity was recorded from the median and ulnar RPNIs using percutaneous thin wire electrodes while the patient performed several hand movements. As expected, the RPNIs produced EMG in response to movements that engaged the muscles originally innervated by the amputated nerve.

[0107] Referring to FIG. 17, graphs 1700 and 1702 show signals recorded from the median nerve RPNI, the ulnar nerve RPNI, and a healthy wrist muscle (Flexor Carpi Ulnaris - FCU) during two different hand movements. Specifically, graph 1700 shows signals recorded during a thumb-pinky counteraction, which corresponds to placing the tip of the thumb against the tip of the pinky. Graph 1702 shows signals recorded during a thumb counteraction, which corresponds to placing the tip of the thumb against the base of the pinky without bending the pinky. These graphs illustrate that physiologically correct signals were obtained from the RPNI. In other words, the nerves are activated during the correct movements.

[0108] As expected, the median RPNI signal (shown in the top row of graphs 1700 and 1702) displays similar activity amplitudes during both thumb-pinky opposition and thumb-only opposition. This is because the median nerve originally innervated the muscles of the thumb but not the muscles of the pinky. The ulnar RPNI signal (shown in the bottom row of graphs 1700 and 1702) is more activated during thumb-pinky opposition than during thumb-only opposition because the ulnar nerve originally innervated the muscles dedicated to the pinky to a greater extent than the muscles dedicated to the thumb. Finally, healthy FCU muscle activity is clearly present but not correlated to either movement since it is entirely dedicated to wrist flexion.

[0109] Taken together, graphs 1700 and 1702 show that the RPNI is innervated by the expected nerves, as it would be impossible to achieve the same pattern of activity through healthy, intact muscles surrounding the RPNI.

[0110] 18, graph 1800 shows signals recorded from the ulnar RPNI during a key pinch motion, corresponding to making a fist with the thumb pressed against the lateral surface of the index finger, the shape made when inserting a key into a car ignition switch. Graph 1800 illustrates the high signal amplitude achievable through the RPNI technique. For example, the signal-to-noise ratio (SNR) of the data in graph 1800 is 8.65.

[0111] "Example 3" Study of continuous position control in non-human primates In the following example, a monkey's neural signals were detected and monitored while the monkey flexed and extended its fingers. The neural signals were processed using the techniques described above with respect to the present teachings, including the use of a Kalman filter, and the percentage of flexion was predicted based on the neural signals. Additionally, the actual percentage of flexion of the monkey's fingers was monitored and compared to the predicted percentage of flexion.

[0112] Referring to FIG. 19, a graph 1900 shows a predicted percentage of flexion 1902 graphed over time along with the actual observed percentage of flexion of the finger. In the graph 1900, the predicted percentage of flexion 1902 correlates to the actual percentage of flexion 1904 by a correlation coefficient of 0.87. The graph 1900 illustrates the accuracy of the techniques described above with respect to the present teachings, including the use of a Kalman filter, when predicting the actual percentage of flexion based on monitored neural signals. In this manner, the techniques of the present teachings can be used for continuous position control of a prosthetic device. In other words, the present teachings can be used to process neural signals and control a prosthetic device through various flexion positions as opposed to individual prosthetic device positions or states, such as flexion or extension states.

[0113] With reference to Figures 20-30, additional embodiments of the present disclosure are shown, including systems and methods for controlling a prosthetic device based on an amplified neural signal. With reference 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 with an implant device 20 that receives a neural signal from a nerve 6, such as a peripheral nerve, that has been amplified by a free tissue graft 10, as described above. The signal is communicated from the free tissue graft 10 to the implant device 20 via electrical leads 18, such as wires 18a, 18b, 18c, described above with reference to Figure 1. The implant device 20, wires 18a, 18b, 18c, the free tissue graft 10, the prosthetic hand 110, and the neural interface system 4 for amplifying the neural signal from the nerve 6 are described above with reference to Figures 1-7, for example.

[0114] System 200 is similar to the systems described above with reference to Figures 1-7, except that implant device 20 of system 200 wirelessly communicates data related to the amplified neural signals, such as processed EMG data, to prosthetic controller 220, as described in further detail below. Prosthetic controller 220 controls actuators 112 (shown in Figure 3) of prosthetic hand 110 based on signals received from implant device 20 in the same manner that implant device 20 controls prosthetic hand 110, as described above with reference to Figures 1, 3, 5-7, and 9.

[0115] Thus, the present disclosure provides a system and method for controlling a prosthetic hand 110 using amplified nerve signals from a free tissue graft 10. The system and method of the present disclosure provide intuitive functional control of an articulated prosthetic hand for upper limb amputee patients who have undergone a regenerative peripheral nerve interface (RPNI) surgical procedure. As discussed above, the RPNI surgical procedure places individual small muscle grafts or free tissue grafts 10 at the end of nerve bundles in the remaining limb. The nerve bundles reinnervate their respective muscle grafts to form healthy, stable and long-lasting neuromuscular connections. Intramuscular bipolar electrodes, described further below, can be used to record directly from the RPNI. Although the present disclosure describes example embodiments utilizing RPNI, the system and method of the present disclosure can also be used in patients who have undergone a targeted muscle reinnervation (TMR) surgical procedure. The TMR procedure involves transferring residual nerves from an amputated limb to reinnervate new target muscles from a subject that would otherwise have lost their function. The reinnervated target muscle thus serves as a biological amplifier of the amputated neuromotor signal. An electrode can thus be affixed or implanted in the target muscle and connected to the implant device 20 of the present disclosure, which can receive the neural signals generated by the reinnervated nerve in the target muscle and process the signals according to the present disclosure to control a prosthetic device. Furthermore, the implant device 20 can communicate signals to the reinnervated nerve of the target muscle to provide sensory feedback stimulation, as described below. Thus, the systems and methods of the present disclosure, including the implant device 20, can be used in patients who have undergone RPNI and in patients who have undergone TMR procedures.

[0116] As described above, the device is a fully implantable recording system capable of wirelessly transmitting electromyography (EMG) signals to an external device, such as the prosthetic controller 220, for upper extremity prosthetic control. For example, the system can include (1) implantable electrodes and electrical leads 18, (2) an implantable sensing unit, such as the implant device 20, (3) a wireless transmitter, such as included in the communication circuitry 50 of the implant device 20, (4) a wireless receiver, such as included in the communication circuitry 222 of the prosthetic controller 220, (5) an external smart link controller, such as the prosthetic controller 220, and (6) a charging unit, such as an external programmer charger 280 and an inductive charging pad 282, described in further detail below with reference to Figures 20-30. As a further example, the implant device 20 can be implemented with a modified PMA approved spinal cord stimulation device (Nuvectra Algovita, PMA P130028). Yet instead of stimulation, the unit can be configured to record EMG signals from both the remaining muscles and the RPNI using electrode leads. The sensing unit or implant device 20 can wirelessly transmit signals to an external smart controller, such as the prosthetic controller 220, which then decodes or interprets the EMG signals and sends commands to an end device, for example a myoelectric prosthetic hand, such as the prosthetic hand 110. Additionally or alternatively, the implant device can be configured as a stimulation device with a receiver for receiving signals from the transmitter of the prosthetic controller 220 and for outputting signals to the implantable electrodes / electrical leads 18, which are then amplified by the neural interface system 4 to stimulate the nerves 6.Thus, in addition to detecting nerve signals and communicating signals from the implant device to the prosthetic controller, the systems and methods disclosed herein can also be utilized to wirelessly communicate signals from the prosthetic controller 220 to the implant device 20 that are then used for stimulation of the nerve 6.

[0117] With further reference to FIG. 21 , the implant device 20 includes the amplifier 24, the processing circuit 22, and the communication circuit 50 described above. The implant device 20 includes a battery 230 connected to the amplifier 24, the processing circuit 22, and the communication circuit 50. Although a single amplifier 24 is shown in FIG. 21 , additional amplifiers may be used, as discussed below. The implant device 20 receives an amplified neural signal 240 from the free tissue graft 10 via the electrical lead 18. The implant device 20 processes the amplified neural signal, as described in further detail below, and wirelessly communicates data related to the amplified neural signal, such as the processed EMG data, to the prosthetic controller 220 via the communication circuit 50. The communication circuit 50 includes, for example, a wireless transmitter configured for wireless communication. The prosthetic controller 220 includes a communication circuit 222 including, for example, a wireless receiver configured for wireless communication. Wireless communications between the communications circuitry 50 of the implant device 20 and the communications circuitry 222 of the prosthetic controller 220 may be implemented using the Medical Implant Communication Service (MICS) protocol and / or the Medical Device Radio Communication Service (MedRadio) protocol. Additionally or alternatively, any other suitable wireless communications protocol may be used, including, for example, Bluetooth, Bluetooth Low Energy (BLE), WiFi, Near Field Communication (NFC), or other suitable wireless communications protocols.

[0118] The processing circuitry 224 of the prosthetic controller 220 is configured to process signals received by the communication circuitry 222 from the implant device 20 and to communicate, via a data communication bus, such as a CAN bus 226, a prosthetic control signal output 56 for controlling the actuator 112 (shown in FIG. 3) of the prosthetic hand 110. For example, the processing circuitry 224 of the prosthetic controller 220 may implement the control algorithm 900 described above with reference to FIG. 9 for controlling a prosthetic limb based on signals received from the free tissue graft 10. Additionally or alternatively, as discussed above, the prosthetic controller 220 is further configured to process a stimulus feedback signal input, such as a prosthetic sensor input signal 58 generated by one or more pressure sensors 225 of the prosthetic hand 110, corresponding to pressure sensed by the pressure sensor 225 of the prosthetic hand 110. For example, as the prosthetic hand 110 grasps an object, the pressure sensor 225 detects the pressure generated from grasping the object. The prosthetic sensor input signals 58 can be received via a data communication bus such as a CAN bus 226 and processed by the processing circuitry 224 of the prosthetic controller 220, which can then communicate stimulation / sensory feedback signals to the implant device 20 via wireless communication transmitted from the communication circuitry 222 to the communication circuitry 50.

[0119] Referring to FIG. 22, four electrical leads 18 are shown, each of which includes two wires connected to a positive electrode 252 and a negative electrode 254. FIG. 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 FIG. 22, eight wires of the electrical leads 18 are connected to an eight-contact connector 250. In particular, the eight contact wires of the eight-contact connector 250 are connected to exposed wires of the electrical leads 18 with platinum crimps that are then sealed with, for example, silicone tubing, as shown in FIG. 22. Although the example of FIG. 22 utilizes platinum crimps 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. 22 utilizes four two-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, the contact connectors can include contacts to accommodate 2, 4, 6, 10, 12, or any other suitable number of electrical wires, and the electrical leads can include 2, 4, 6, 10, 12, or any other suitable number of electrical wires.

[0120] To record EMG signals from the remaining muscles and RPNI, the system 200 utilizes an electrode and lead assembly that connects muscle tissue, such as the free tissue graft 10, to the implant device 20. The assembly can include a modified version of an existing bipolar electrode and a modified version of an existing cable lead that connects to the implant device 20. Each implant device 20 can connect to, for example, 12 bipolar electrodes, with each lead including eight contacts with three leads per implant device 20. The bipolar electrodes can be modified from the PermaLoc™ electrodes, which are approved by the FDA as part of the long-term implant system NeuRX Diaphragm Pacing System. Compared to the monopolar PermaLoc™ electrodes, the bipolar electrodes have an additional de-insulated lead surface and the plastic plated anchor at the distal tip of the electrode is removed, as shown in FIG. 23. The basic design, all material parts, and sterilization protocols remain the same. Electrodes can be percutaneously implanted, for example in patients who have undergone upper limb amputation, to record EMG signals from the remaining muscles and the RPNI.

[0121] Several modifications and additions were made to ensure the bipolar electrodes were fully implanted and connected to the implant device 20. An industry standard Bal Seal connector lead made by Cirtec Biomedical, Inc. was used to connect to the implant device 20. A single Bal Seal connector lead has eight contacts with the lead body and eight conductors in parallel. Other numbers of contacts, such as 12 conductors, can be used. The lead diameter is 0.053 mm, resulting in 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 ETFE conductor insulation. The electrical connection to the Bal Seal uses a platinum-iridium connector ring. The bare cable is exposed 10 to 15 cm from the proximal end of the Bal Seal connector lead, exposing the bare cable at the proximal end of the PermaLoc bipolar lead. The distal and proximal ends of the two leads are permanently interdigitated to form a single lead with four bipolar electrodes. The interdigitation will connect each individual bare wire using a platinum crimp and be covered with silicone tubing for protection. The protected joint will be placed as close as possible to the implant device 20 to avoid crossing the thicker portion of the lead with the anatomical joints in the patient's body. The total length of the assembled lead from proximal to distal end is approximately 100 to 105 cm. Thus, the distal and proximal ends of the two previously approved electrode leads are permanently interdigitated without changing the electrical or material properties of the leads.

[0122] Referring to FIG. 24A, the implant device 20 includes a header that receives one or more contact connectors connected to electrical leads. For example, as shown in FIG. 24A, the implant device includes electrical ports 256 each configured to receive an eight-contact connector 250. The ports 256 can each include a Bal Seal connector, such as the Bal Seal connector manufactured by Cirtec Biomedical, Inc., configured to seal the implant device 20 when the eight-contact connector 250 is inserted into the electrical port. The implant device also includes a header having parallel conductors 254 with contacts that correspond to, are aligned with, and are spaced apart from 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 FIG. 24A, the implant device is configured with a header having three parallel conductors 254 for receiving three eight-contact connectors 250. In this manner, the implant device 20 shown in FIG. 24A can accommodate signals from up to 24 wires, i.e., three sets of eight electrical wires based on 24 electrodes including 12 positive electrodes 252 and 12 negative electrodes 254. The 12 positive electrodes 252 and 12 negative electrodes 254 allow the implant device 20 to receive 12 bipolar channels of EMG activity based on the amplified neural signals 240 from the free tissue graft 10. While the example of FIG. 24A utilizes 24 wires and electrodes, the implant device 20 can be configured with headers and parallel conductors 254 to accommodate any number of wires and electrodes. For example, the implant device 20 can include a header with two sets of parallel conductors 254, each set including 12 contacts for receiving a 12-contact connector.Alternatively, implant device 20 may include four sets of parallel conductors 254 for receiving four eight-contact connectors 250 such that signals may be received from four sets of electrical wires based on signals from 32 electrodes including 16 positive electrodes 252 and 16 negative electrodes 254. In this manner, implant device 20 may include a header with any suitable number of parallel conductors 254 with any number of contacts for receiving contact connectors with any number of wires.

[0123] The implant device 20 can be implemented using a modified implantable recording device based on the Algovita platform developed by Cirtec Biomedical, Inc. The Algovita platform (PMA P130028) is an FDA approved implantable pulse generator for pain reduction via spinal cord stimulation. The electrode leads are connected to the implant device 20 with a header having three rows, each containing eight spring platinum-iridium bal seals. The leads are secured after implantation with titanium set screws that are inserted through the septum and tightened with a provided torque wrench. The Algovita is internally powered by a 4.1V lithium-ion battery with a nominal capacity of 215mAh. It has been tested to maintain capacity after 1000 discharge cycles. It also has a low self-discharge rate, so shelf life is not an issue for this application as the battery will exceed the shelf life of the sterile packaging. The implant device 20 communicates wirelessly with external components via a Medical Implant Communication System (MICS) that includes communications circuitry 50, which may include, for example, a Microsemi ZL70323MNJ, a newer version of the MICS chip used in the original Algovita. MICS communications uses the 2.45 GHz band for wake-up and the 402 to 405 MHz band for data transfer. Except for the updated MICS chip, the battery, power management, and communications circuitry all remain unchanged.

[0124] The external physical design of the implant device 20 remains the same as the Algovita device. There are limited changes to the internal components to convert the Algovita into a sensing device. The Saturn stimulator ASIC has been replaced with an Intan RHD2216 amplifier to sense biopotentials rather than stimulating neural tissue. Intan amplifiers have low power consumption and have been used previously in surgically invasive clinical studies. No adverse events were noted in these acute studies. As an illustration, a linear voltage regulator (TPS7A2033, Texas Instruments) can utilize the 4.1V battery voltage as input and convert this to a 3.3V signal to power the RHD2216. The (ZL70321MNJ, Microsemi) MICS module on the Algovita has been replaced with a newer version from Microsemi, the ZL70323MNJ.

[0125] Referring to FIG. 24B, another embodiment of an alternative arrangement is shown that includes six electrical leads 18' for six bipolar electrodes connected to a twelve-contact connector 250'. The twelve-contact connector 250' can be received by a header of the implant device 20, similar to the configuration described above with respect to FIGS. 22-24A. As mentioned above, the port 256 of the implant device 20 can include a Bal Seal connector, such as a Bal Seal connector manufactured by Cirtec Biomedical, Inc., configured to seal the implant device 20 when the twelve-contact connector 250' is inserted into the electrical port of the implant device 20. The contacts of the twelve-contact connector 250' are sufficiently spaced apart to match the spacing of the corresponding connectors of the header of the implant device 20. Additionally, in the embodiment of FIG. 24, a triangular coupling device 251 is utilized to receive and couple the six electrical leads 18' to the twelve-contact connector 250'. As shown in FIG. 24B, the coupling device 251 includes parallel connections for six electrical leads 18' on the wide portion of the triangular coupling device 251 and couples the twelve wires for the six bipolar electrodes into a single cable, each of the twelve wires being connected to an individual contact of the twelve-contact connector 250'.

[0126] Referring to FIG. 25, the implant device 20 includes a protection circuit for protecting electrical components of the implant device from surge voltages during an electrostatic discharge (ESD) event. For example, the implant device may experience an ESD event while the implant device 20 is being handled during a surgical procedure for implanting the implant device 20 in a patient's body. As shown in FIG. 25, for example, a 220 ohm resistor 262 is placed before the input to the amplifier 24 to provide protection against surge voltages that may build up in the event of an ESD event. Illustratively, the 220 ohm resistor 262 can be an AC0603JR-07220RL resistor available from Yageo. As a further example, as shown in FIG. 25, the amplifier 24 can be an Intan RHD2216 amplifier for sensing biopotentials. Additionally, as a further example, an ESD diode 260 can be placed on the other side of the resistor 262 across from the input to the amplifier 24. The diode 260 can be, for example, an SMS24T1G ESD diode available from On Semiconductor. Although the ESD protection circuit is illustrated in FIG. 25 for one input to the implant device 20, the same protection circuit including the 220 ohm resistor 262 and the ESD diode 260 can be included for each input to the implant device 20. Thus, for an implant device 20 configured with a header having three sets of eight-contact connectors 250, i.e., three sets of contactors 254 for a total of 24 inputs, as shown in FIGS. 21-24, the implant device 20 can include 24 sets of the protection circuit shown in FIG. 25. In other words, the implant device 20 can include one 220 ohm resistor 262 and one ESD diode 260 for each of the two inputs of the implant device 20.Although specific examples of resistor 262 and diode 260 are shown in FIG. 25, any other suitable resistor and diode can be used for the protection circuit to protect implant device 20 from ESD events.

[0127] Referring to FIG. 26, a functional block diagram of a system and method for processing an amplified nerve signal according to the present disclosure is illustrated. As shown in FIG. 26, a raw amplified nerve signal 240 is received by the implant device 20. For example, as shown in FIG. 21 through FIG. 24 and discussed above, the implant device 20 receives 12 bipolar channels of EMG activity from the implanted electrodes and electrical leads 18. At 270, the amplified nerve signal is filtered using a 100 to 500 Hz bandpass filter that 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 implant device 20 at a Nyquist rate of approximately 1 kHz, resulting in approximately 1 kila sample per second (1 KSps) of EMG data. At 274, the processing circuitry 22 of the implant device 20 then calculates the mean-absolute-value (MAV) for each bipolar channel of activity. At 276, the processing circuitry 22 uses the communications circuitry 50 of the implant device 20 to wirelessly transmit the MAV for each activity on the bipolar channel to the communications circuitry 222 of the prosthetic controller 220 using the MICS protocol.

[0128] To wirelessly transmit and receive data to and from a non-implanted device, such as the prosthetic controller 220 or another external communication device, the implant device 20 can use the communication circuitry 50 of the existing Microsemi MICS communication device from the Algovita platform, as discussed above. Following implantation, the implant device 20 can be turned on and off by waving a magnet over the device. When the implant device 20 is operational, it can interact with two external devices: a programmer charger, discussed below, and the prosthetic controller 220, also referred to as a Smart Link Controller (SLC). The implant device 20 is configured to wirelessly stream EMG to the prosthetic controller 220, as discussed above. The implant device 20 includes a bootloader for wireless firmware updates. The microcontroller of the implant device 20 is a Texas Instruments MSP430F2618 (MSP430), also used in the Algovita platform. The microcontroller is programmed to record 12 bipolar channels of EMG activity from the implanted electrodes using Intan RHD2216 amplifiers.

[0129] In this manner, rather than sending the raw EMG data to the prosthetic controller 220, the implant device 20 first filters, samples, and processes the raw EMG signals from the bipolar channels to calculate the MAV of the sampled EMG signals per bipolar channel, and then wirelessly transmits only the processed MAV data per bipolar channel to the prosthetic controller 220. This configuration provides the technical advantage of maximizing the battery life of the battery 230 of the implant device 20, and of the power source of the prosthetic device itself, as the implant device 20 would require much more power to stream the higher bandwidth raw EMG signals directly from the implant device 20 to the prosthetic controller 220. In this manner, the systems and methods of the present disclosure maximize the battery life of the device by first filtering, sampling, and processing the raw EMG data at the implant device 20, and then wirelessly transmitting only the processed EMG signals per bipolar channel to the prosthetic controller 220.

[0130] With reference to Figure 27, an example of a prosthetic controller 220 is shown both on the outside of the prosthetic hand 110 and installed within the prosthetic hand 110. A quarter coin is also shown in Figure 27 to illustrate the relative size and scale of the prosthetic controller 220. As shown in Figure 27, the prosthetic controller 220 can be implemented using a printed circuit board assembly (PCBA).

[0131] The prosthetic controller 220 receives the processed EMG packets and decodes them into movement commands for the prosthetic. The prosthetic controller 220 includes a communication circuit 222, which may include, for example, a ZL70123 chip, for wirelessly and securely communicating with the implant device 20 using, for example, standard MICS protocols. All components of the prosthetic controller 220 are soldered onto a PCBA and the device is enclosed in a water-resistant enclosure within the prosthetic. The materials used in the prosthetic controller 220 are typical of modern PCBAs. The circuit board is a flat laminate composite with internal copper circuitry. The components mounted on the circuit board are application specific integrated circuits (ASICS) that are used as intended by the manufacturer. The ASICS are attached to the board with lead-free solder using industry standard surface mount technology.

[0132] The prosthetic controller uses state-of-the-art algorithms, including machine learning algorithms, to decode the EMG signals into motor commands, as described above. The choice of algorithm depends on the operating mode of the prosthetic hand. Classifiers may be used to decode grasps for intuitive grasp selection, with further advances for corresponding control. Alternatively, regressors may be used to simultaneously and independently control individual fingers.

[0133] The prosthetic controller can be connected to a laptop, such as the external programmer device 290 described below, to record processed EMG from the implant device 20. The implant device 20 can also be wirelessly connected to and communicate with a laptop, such as the external programmer device 290, as discussed below. The recorded EMG can be used to measure the signal strength from each electrode pair and calculate decode parameters. A configuration mode can be used to load new parameters into the prosthetic controller 220. A test mode is available to verify the prosthetic controller functionality of the implant device 20. In the test mode, the external programmer device 290 can transfer pre-recorded EMG packets to the prosthetic controller, which will respond with decoded movement commands. Otherwise, the operation of the device is fixed, including receiving signals, decoding the intended movements, and sending outputs to the prosthetic.

[0134] Power for the prosthetic controller 220 is provided by the prosthetic battery. Upon start-up, the prosthetic controller 220 automatically connects to the prosthetic hand 110 and waits to receive a signal from the implant device 20. When the prosthetic controller 220 does not receive a signal from the implant device 20, the prosthetic controller 220 will stop sending movement predictions to the prosthetic hand 110. This will prevent unintended or unpredictable movement of the prosthetic.

[0135] Referring to FIG. 28, an external programmer charger 280 and an electromagnetic inductive charging pad 282 for charging the implant device 20 are shown. To charge the implant device 20 after it is surgically implanted in the patient, the electromagnetic inductive charging pad 282 can be aligned on the patient near the location of the implant device 20 within the patient's body. For example, as shown in FIG. 20, if the implant device 20 is implanted in the patient's thorax, the electromagnetic inductive charging pad 282 can be placed on the patient's thorax above the approximate location of the implant device 20 within the patient's thorax. The external programmer charger 280 and / or the electromagnetic inductive charging pad 282 generate an output, such as illuminating a light emitting diode (LED) or generating an audible sound, to indicate that the electromagnetic inductive charging pad 282 and the implant device 20 are aligned for inductive charging. Once aligned and in communication, the external programmer charger 280 and the electromagnetic inductive charging pad 282 can charge the battery 230 of the implant device 20 while the implant device 20 is implanted in the patient's body. The same patient programmer charger (PPC) from the Algovita platform can be used to wirelessly recharge the ISU's internal battery using inductive coupling at 40 kHz at 0.65 W. The PPC can also be used to check the battery level of the ISU.

[0136] 29, an external programmer device 290 is shown in communication with the communication circuitry 50 of the implant device 20 and the communication circuitry 222 of the prosthetic controller 220. The communication between the external programmer device 290 and the communication circuitry 50 of the implant device 20 can be performed wirelessly while the implant device 20 is implanted in the patient's body. The communication between the external programmer device 290 and the communication circuitry 222 of the prosthetic controller 220 can be performed wirelessly or via a wired communication connection. The external programmer device 290 can be implemented in a computing device, such as a laptop computer, a desktop computer, a tablet device, a mobile device, or any other suitable computing device configured with an application configured to communicate with the implant device 20 and the prosthetic controller 220. The external programmer device 290 can receive the EMG signal of the amplified neural signal 240 from the implant device 20, including, for example, a raw EMG signal or a filtered, processed, and sampled EMG signal, as described above. As a further example, the external programmer device 290 can determine configuration parameters of the prosthetic controller 220 using the filtered, processed, and sampled EMG signals from the implant device 20. Additionally or alternatively, the external programmer device 290 can receive the filtered, processed, and sampled EMG signals from the communication circuitry 222 of the prosthetic controller 220 after the filtered, processed, and sampled EMG signals are received from the implant device 20. The external programmer device 290 can then determine configuration parameters to be used by the prosthetic 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 prosthetic controller 220 may also use machine learning algorithms to determine and adjust configuration parameters used by the prosthetic controller 220 to control the prosthetic hand 110 based on the filtered, processed, and sampled EMG signals from the implant device 20.

[0137] Referring to FIG. 30, another embodiment of the present disclosure is shown. The system 300 of FIG. 30 is similar to the system 200 described above, except that the system 300 does not include an implant device 20 implanted in a patient. Instead, the system 300 of FIG. 30 is configured for testing and configuration before the implant device 20 is implanted in a patient. Similar to the system 200 described above, the system 300 includes a neural interface system 4 with a neural signal from a nerve 6, such as a peripheral nerve, amplified by a free tissue graft 10, as described above. In the system 300 of FIG. 30, the electrical lead 18 still extends outside the patient's body through a port 302 and connects to an interface 304. The interface 304 is connected to an external programmer device 290 that communicates with the prosthetic controller 220 of the prosthetic hand 110. The system 300 can be used to test and calibrate parameters of the prosthetic controller 220 before the implant device 20 is implanted in the patient's body, and with or without the prosthetic hand 110 affixed to the patient. In this manner, the system 300 can be used to review and analyze the nerve signal generated by the nerve 6 and amplified by the free tissue graft 10 to determine whether the signal should be sufficient for use by the prosthetic controller 220 to control the prosthetic hand 110 before the implant device is implanted in the patient.

[0138] As discussed above, the implant device 20 of the present disclosure can be used to both generate prosthetic control signal outputs 56 used to control a prosthetic device, such as the prosthetic hand 110, and receive stimulation feedback signals, such as prosthetic sensor input signals 58 generated by one or more pressure sensors 225, used to provide stimulation and sensory feedback signals to the nerve 6. For example, the implant device 20 can include one or more Intan RHS2116 stimulator / amplifier chips configured to stimulate neural tissue and sense biopotentials. The RHS2116 stimulator / amplifier chip provides current-controlled stimulation pulses to individual electrode contacts up to 2.55 milliamps (mA), which have been shown to be sufficient to elicit sensory perception representations. The stimulation pulses can be delivered in a biphasic manner for charge balance. The RHS2116 stimulator / amplifier chip has a fast amplifier reset feature to remove stimulation artifacts prior to sensing biopotentials. The RHS2116 stimulator / amplifier chip can be powered by a 3.3 volt linear voltage regulator, such as the TPS7A2033 by Texas Instruments. Additionally or alternatively, a bipolar output power supply can be used to provide the RHS2116 stimulator / amplifier chip with positive and negative voltage sources for stimulation, up to plus or minus seven volts (+ / -7V).

[0139] In one embodiment, the systems and methods of the present disclosure can be used to both (1) receive signals from a free tissue graft to generate a prosthetic control signal output 56 used to control a prosthetic device, such as a prosthetic hand 110, and (2) receive stimulation and sensory feedback signals, such as a prosthetic sensor input signal 58 generated by one or more pressure sensors 225 of a prosthetic device, such as a prosthetic hand 110. Nevertheless, one problem with simultaneously performing simultaneous motor control and sensory perception / stimulation functions is that the stimulation signals may create artifacts or noise when signals are sensed and recorded for the motor control of the prosthetic device. Thus, the implant device 20 of the present disclosure is configured to mitigate and / or eliminate signal corruption and artifact problems using one or more of the approaches and algorithms described below with reference to Figures 31-33, which illustrate different approaches and algorithms for mitigating and / or eliminating signal corruption and artifacts when signals are sensed and recorded for the motor control of the prosthetic device.

[0140] For example, referring to FIG. 31, an algorithm for generating prosthetic motion commands and performing stimulation within alternating time periods is illustrated. In FIG. 31-FIG. 33, from left to right along the horizontal axis, time periods t-3, t-2, t-1, and t are illustrated. The time periods can be, for example, 10 to 50 milliseconds in duration, although any suitable time period length can be used. Further, X represents the prosthetic motion command generated by the implant device 20 during the time periods indicated by the subscripts. Further, Y represents the EMG signal received from the electrodes implanted or affixed to the RPNI, the remaining muscles, and / or the target muscles after the TMR procedure during the time periods indicated by the subscripts. During time period t-3, the stimulation function is initially turned off, and the implant device 20 generates the EMG signal Y. t-3 Based on prosthetic movement command X t-3In time period t-2, a stimulation event is detected that continues throughout example periods t-2, t-1, and 5. The stimulation event can be, for example, receiving a signal from the pressure sensor 225 of the prosthetic device. In other words, stimulation can be initiated and used when a need to provide stimulation to the sensor perception is detected, such as when the pressure sensor 225 detects an increase in pressure on the prosthetic device and generates a pressure signal.

[0141] In the approach and algorithm illustrated in FIG. 31, the implant device 20 alternates between implementing stimulation for sensory perception and generating prosthetic motion commands during successive time periods. For example, stimulation for sensory perception is implemented when the stimulation function is on during time periods t-2 and t, and prosthetic motion commands X are generated. t-2 and X t-1 But the EMG signal Y t-3 and Y t-1 The stimulation function is off when the prosthetic motion command is generated during periods t-3 and t-1 based on the stimulation function. Thus, the prosthetic motion command is generated during periods t-3 and t-1, and the stimulation for sensory perception is performed during periods t-2 and t. Thus, the generation of the prosthetic motion command is temporarily paused during periods t-2 and t while the stimulation is performed. Similarly, the stimulation is temporarily paused during periods t-3 and t-1 while the prosthetic motion command is generated. In the example of FIG. 31, after applying the stimulation and then moving to the next period for generating the prosthetic motion command, such as t-1, the position of the prosthetic device and / or the state of the prosthetic command are in the same position or state as they were at the end of the previous period for generating the prosthetic motion command, such as t-3. In other words, in the approach and algorithm of FIG. 31, the implant device 20 alternates between (A) generating the prosthetic motion command X with the stimulation off state and (B) pausing the prosthetic control with the stimulation on state for the time window. In this manner, the implant device 20 switches back and forth between receiving stimulation and sending control signals at intervals, such as every 10 to 50 milliseconds.

[0142] In the approach and algorithm of FIG. 31, the prosthetic movement commands are not updated during stimulation periods, e.g., t-2 and t. In other words, if the prosthetic movement commands are updated at the end of period t-3, e.g., command X t-3 If we command the prosthesis 110 to approach at a speed instructed by t-3 This approach and algorithm can be implemented with several control algorithms, including regressors that estimate the kinematics, such as linear regression or neural networks, or classifiers that estimate the individual states, such as linear discriminant analysis, Naive Bayes, support vector machines, and / or neural networks.

[0143] Referring to Fig. 32, another algorithm for generating prosthetic motion commands and performing stimulation is illustrated. The approach and algorithm illustrated in Fig. 32 is similar to the approach and algorithm illustrated with reference to Fig. 31, except that the approach and algorithm illustrated in Fig. 32 estimates the prosthetic motion commands during the period when stimulation is being performed. For example, at periods t-2 and t, the implant device 20 estimates X t-3 and X t-1 Based on the previously generated prosthetic movement commands such as t-2 and X tFor example, rather than simply pausing the generation of prosthetic motion commands during the stimulation period, as is done by the approach and algorithm of FIG. 31, the approach and algorithm of FIG. 32 instead provides estimated prosthetic motion commands at periods t-2 and t, etc., to provide a smoother transition to the generation of prosthetic commands at periods t-3 and t-1. This approach and algorithm can utilize recursive or regression algorithms to estimate kinematics, such as Kalman filters or particle filters, or classifiers to estimate discrete states, such as Markov models. In this manner, the implant device 20 recursively estimates the motion state over time using both the incoming signal measurements and mathematical process models. Similar to the approach of FIG. 31, the approach of FIG. 32 alternates between sensing and receiving signals from the electrodes, such as at periods t-3 and t-1, and sending stimulation signals for sensory perception, such as at periods 5-2 and 5. Furthermore, the implant device 20 does not read or sense any signals from the electrodes while performing stimulation, for example, during periods t-2 and 5. The implant device 20 may still provide a smoother sequence of commands over time, e.g., X, X-axis, etc., rather than pausing production of prosthetic movement commands during periods of stimulation. t-2 and X t Thus, the approach and algorithm of FIG. 32 can generate estimated prosthetic movement commands such as, for example, X t-3 and X t-1 , etc., can be interleaved between two consecutively generated commands, avoiding jumps between commands that may occur in the approach and algorithm of Figure 31 when command generation is paused. In this manner, the approach and algorithm of Figure 32 can accommodate intermittent measurements and provide smooth command updates during periods when stimulation for sensory perception is performed.

[0144] Referring to FIG. 33, another algorithm for generating prosthetic motion commands and performing stimulation is illustrated. In the approach and algorithm of FIG. 33, prosthetic motion commands are generated in all periods. Nevertheless, during periods in which stimulation is also performed, such as t-2 and t, the implant device 20 performs artifact estimation to estimate artifacts introduced into the EMG signal Y from the electrodes by the stimulation, then subtracts or filters out the estimated artifacts from the EMG signal Y, and generates the prosthetic motion command X based on the resulting / filtered EMG signal resulting after artifact subtraction. In this way, the implant device 20 can remove artifacts that may have been introduced into the EMG signal as a result of performing stimulation while simultaneously reading neural signals from the electrodes affixed to the free tissue graft 10. In other words, the implant device performs noise reduction, filtering, or cancellation on the EMG signal Y to remove artifacts that may have been introduced into the signal by performing stimulation for sensory perception.

[0145] For example, a template subtraction algorithm can be used to generate expected artifacts based on a given stimulation signal or set of stimulation signals. The template can be used to subtract artifacts from the EMG signal. In other words, in this approach, the EMG signal Y may be corrupted by stimulation for sensory perception, but the algorithm or approach filters the EMG signal Y to subtract artifacts, so that prosthetic movement commands can be generated based on the filtered EMG signal Y. For example, the template subtraction algorithm can be implemented by averaging artifacts immediately after the stimulation pulse using multiple exponential filters with a filter learning rate. In this way, a representative template can be constructed, which is then subtracted from the original signal to generate an estimated artifact-free signal.

[0146] Additionally or alternatively, an ε-normalized least mean squares algorithm can be used to remove unwanted artifacts from the EMG signal Y. For example, the ε-normalized least mean squares algorithm can be implemented as an improved version of the standard least mean squares algorithm, resulting in better performance for signals with larger and lower signal energy intervals. In the ε-normalized least mean squares algorithm, an adaptive filter relies on a reference signal that is highly correlated with the stimulation artifact. The algorithm is able to adapt to various artifact waveforms without requiring complete re-learning of the weights used by the algorithm. For example, the ε-normalized least mean squares adaptive 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 further used to update the filter weights after each sample.

[0147] Although the example of Fig. 33 illustrates stimulation being performed in alternating periods, stimulation can also be performed continuously in successive periods while reading the EMG signal Y and filtering / subtracting any artifacts introduced in the EMG signal Y. Nevertheless, stimulation can be performed in alternating periods as shown in the example of Fig. 33 in order to conserve battery power of the implant device 20.

[0148] While any single algorithm or approach of FIGS. 31-33 can be used, implant device 20 can alternatively be configured to implement all three approaches and / or switch between the approaches to determine and select the best approach or algorithm for a particular environment or scenario.

[0149] 31-33 illustrate an approach in which stimulation for sensory perception is performed in alternating periods, other sequences can alternatively be used, for example, stimulation can be performed in two or more consecutive periods prior to one or more periods of reading the EMG signal Y to generate prosthetic movement commands.

[0150] A non-limiting discussion of terminology The foregoing description of the embodiments has been provided for illustration and explanation. It is not intended to be exhaustive or to limit the present disclosure. Individual elements or features of a particular embodiment are generally not limited to this particular embodiment, and where applicable, even if not specifically shown or described, can be interchangeable and used in selected embodiments. The same can also be modified in various ways. Such modifications should not be considered as a departure from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure. The example embodiments are provided so that the disclosure will be complete and will fully convey the scope to those skilled in the art. Numerous specific details are described, such as examples of specific components, devices, and methods, to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that the example embodiments can be embodied in many different forms, and neither should be taken to mean limiting the scope of the present disclosure. In some example embodiments, well-known processes, well-known device structures, and well-known technologies are not described in detail.

[0151] As used herein, the term circuitry may refer to being part of or including an application specific integrated circuit (ASIC), an electronic circuit, a combinatorial logic circuit, a field programmable gate array (FPGA), a processor (shared, dedicated, or a 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-a-chip. The term circuitry may include memory (shared, dedicated, or a group) that stores code executed by a processor.

[0152] The term code as used above may include software, firmware, and / or microcode and may refer to programs, routines, functions, classes, and / or objects. The term shared as used above means that some or all code from multiple circuits may be executed using a single (shared) processor. Furthermore, some or all code from multiple circuits may be stored by a single (shared) memory. The term group as used above means that some or all code from a single circuit may be executed using a group of processors. Furthermore, some or all code from a single circuit may be stored using a group of memories.

[0153] The apparatus and methods described herein may be implemented by one or more computer programs executed by one or more processors. A computer program includes processor-executable instructions stored on a non-transitory tangible computer-readable medium. A computer program may also include stored data. Non-limiting examples of non-transitory tangible computer-readable media are non-volatile memory, magnetic storage, and optical storage.

[0154] The headings and subheadings used herein (such as "Background" and "Summary") are intended only for general organization of topics within the disclosure and are not intended to limit the disclosure or any aspect thereof. In particular, the subject matter disclosed in the "Background" may include novel technology and is not to be considered a recitation of prior art. The subject matter disclosed in the "Summary" is not an exhaustive or complete disclosure of the entire scope of the technology or any embodiment thereof. The classification or discussion of materials within sections of this specification as having particular utility is done for convenience, and no inference should be drawn that the materials must necessarily or singularly function according to their classification herein when used in any given configuration.

[0155] The disclosures of all patents and patent applications referenced or cited in this disclosure are hereby incorporated by reference.

[0156] The description and specific examples, while indicating features and embodiments, are intended for illustration purposes only and are not intended to limit the scope of the present disclosure. Moreover, the recitation of multiple embodiments having stated features is not intended to exclude other embodiments having additional features or other embodiments incorporating different combinations of the stated features. The specific examples are provided for illustration of how to implement and use the described methods, systems, and compositions and are not intended as representations that a given embodiment has been performed or tested, or has not been performed or tested, unless expressly stated otherwise.

[0157] As used herein, the words "prefer" or "preferable" refer to embodiments that provide certain benefits, under particular circumstances. Nonetheless, other embodiments may be preferred, under the same or other circumstances. Further, the recitation of one or more preferred embodiments does not imply that other embodiments are not beneficial, and is not intended to exclude other embodiments from the scope of the present disclosure.

[0158] As used herein, the word "include" and variations thereof are intended to be non-limiting, and thus the recitation of items in a list is not exclusive of other similar items that may also be beneficial in the described methods, systems, materials, compositions, and devices. Similarly, the terms "can" and "may" and variations thereof are intended to be non-limiting, and thus the recitation that an embodiment can or may include a particular element or feature does not exclude other embodiments that do not include the element or feature.

[0159] Although the term "comprising," which can be broadly interpreted as a synonym for non-limiting terms such as including, containing, or having, is used herein to describe and claim embodiments of the present disclosure, the embodiments may alternatively be described using more restrictive terms such as "consisting of" or "consisting essentially of." Thus, for any given embodiment that recites materials, components, or process steps, the present disclosure also specifically includes embodiments that consist of, or consist essentially of, such materials, components, or processes, excluding additional materials, components, or processes (if consisting of), and excluding additional materials, components, or processes that affect the critical properties of the embodiment (if consisting essentially of), even if such additional materials, components, or processes are not explicitly recited in this application. For example, a recitation of a composition or process reciting elements A, B, and C specifically contemplates the embodiment consisting of, as well as consisting essentially of, A, B, and C, and excludes element D, which may be recited in the art even if element D is not expressly described herein as excluded.

[0160] As referred to herein, ranges are inclusive of the endpoints unless otherwise specified, and include the disclosure of all distinct values ​​within the entire range, as well as further divided ranges. Thus, for example, a range of "A to B" or "about A to about B" is inclusive of A and B. The disclosure of a value and range of values ​​of a particular parameter (such as temperature, molecular weight, weight percent, etc.) does not exclude other values ​​and ranges of values ​​useful herein. The use of the term "about" with respect to a range, value, or threshold should be considered in the context of the range, value, or threshold as understood by one of ordinary skill in the art. To the extent that a range, value, or threshold cannot be determined from the context, the use of the term "about" can correspond to a range of 10 to 15 percent. It is contemplated that two or more specific example values ​​for a given parameter may define the endpoints of a range of values ​​that may be claimed for the parameter. For example, if a parameter X is exemplified herein as having a value A and also exemplified herein as having a value Z, it is contemplated that the parameter X may have a range of values ​​from about A to about Z. Similarly, the disclosure of two or more ranges of values ​​for a parameter is contemplated to encompass all possible combinations of value ranges that may be claimed using the endpoints of the disclosed ranges (whether such ranges are nested, overlapping, or entirely different). For example, if a parameter X is exemplified herein as having a value in the ranges 1 to 10, or 2 to 9, or 3 to 8, it is also contemplated that parameter X may 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. 1. An implant device having a processing circuit configured to receive electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of a subject, the electrical signals being received through a plurality of pairs of bipolar electrodes, each having a positive electrode and a negative electrode, implanted inside the plurality of free tissue grafts and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being tissue autografts that are removed from the subject, devascularized, denervated, and then surgically affixed to the subject, the processing circuit being further configured to process the received electrical signals, generate processed signal data, and wirelessly transmit the processed signal data to a prosthetic controller. Equipped with the nerve being reinnervated from the plurality of free tissue grafts after the plurality of free tissue grafts have been surgically attached to the nerve; the electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater; The system, wherein the prosthetic controller is configured to control a prosthetic device based on the processed signal data wirelessly transmitted from the processing circuitry of the implant device.

2. The system of claim 1 , wherein the processing circuitry is configured to apply a bandpass filter to the received electrical signal and to sample the filtered electrical signal.

3. 3. The system of claim 2, wherein the bandpass filter is a 100 to 500 Hz bandpass filter, and the processing circuitry is configured to sample the filtered electrical signal at a sampling rate of 1 kHz.

4. 4. The system of claim 3, wherein the processing circuitry is further configured to generate the processed signal data by calculating a mean absolute value of the sampled electrical signals and to wirelessly transmit the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic device controller.

5. 10. The system of claim 1, wherein the implant device includes a protection circuit for protecting the implant device from surge voltages during an electrostatic discharge event during implantation of the implant device in a patient.

6. The system of claim 5 , wherein the protection circuit comprises at least one resistor and at least one diode.

7. 2. The system of claim 1, wherein the plurality of bipolar electrode pairs includes a first set of bipolar electrode pairs and a second set of bipolar electrode pairs, the first and second sets of bipolar electrode pairs respectively connected to the implant device with first and second multi-contact connectors each having a pair of contacts associated with each bipolar electrode pair, and the implant device includes headers each having first and second ports configured to receive the first and second multi-contact connectors, respectively.

8. 8. The system of claim 7, wherein the first and second ports include a Bal Seal connector configured to seal the implant device 20 when the first and second multi-contact connectors are inserted into the first and second ports, respectively.

9. 2. The system of claim 1, wherein the prosthetic device includes at least one pressure sensor, the prosthetic controller is configured to receive at least one pressure signal from the at least one pressure sensor and communicate the pressure signal to the implant device, and the processing circuitry of the implant device is further configured to receive the pressure signal and generate and transmit at least one stimulation signal to at least one of the plurality of bipolar electrode pairs to stimulate the nerve of the subject through at least one of the free tissue grafts affixed to the nerve of the subject.

10. 10. The system of claim 9, wherein the processing circuitry of the implant device is configured to alternate between the receiving of the electrical signal and the generating and transmitting of the at least one stimulation signal during successive time periods.

11. 11. The system of claim 10, wherein the processing circuitry is configured to generate commands for controlling the prosthetic device based on the received electrical signals, and to generate estimated commands for controlling the prosthetic device during a period when the at least one stimulation signal is generated and transmitted to the at least one of the plurality of bipolar electrode pairs.

12. 10. The system of claim 9, wherein the processing circuitry is configured to estimate an artifact in the electrical signal produced by the at least one stimulation signal and to subtract the artifact from the electrical signal.

13. receiving electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of the subject with an implant device having processing circuitry and communication circuitry, the electrical signals being received through a plurality of pairs of bipolar electrodes, each having a positive electrode and a negative electrode, implanted inside the plurality of free tissue grafts and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being tissue autografts that are removed from the subject, devascularized, denervated, and then surgically affixed to the subject; processing the received electrical signal with the processing circuitry; generating processed signal data in said processing circuitry; wirelessly transmitting the processed signal data to a prosthetic device controller via the communications circuitry; Including, the portion of the nerve being reinnervated from the plurality of free tissue grafts after the plurality of free tissue grafts have been surgically attached to the nerve; the electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater; The method, wherein the prosthetic controller is configured to control a prosthetic device based on the processed signal data wirelessly transmitted from the processing circuitry of the implant device.

14. 14. The method of claim 13, further comprising the steps of: applying a bandpass filter to the received electrical signal with the processing circuitry; and sampling the filtered electrical signal with the processing circuitry.

15. 15. The method of claim 14, wherein the bandpass filter is a 100 to 500 Hz bandpass filter and the sampling is performed at a sampling rate of 1 kHz.

16. 16. The method of claim 15, further comprising generating the processed signal data in the processing circuitry by calculating a mean absolute value of the sampled electrical signals, and wirelessly transmitting the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic device controller in a communications circuitry.

17. 14. The method of claim 13, wherein the implant device includes a protection circuit for protecting the implant device from surge voltages during an electrostatic discharge event during implantation of the implant device in a patient.

18. 20. The method of claim 17, wherein the protection circuit comprises at least one resistor and at least one diode.

19. 14. The method of claim 13, wherein the plurality of bipolar electrode pairs includes a first set of bipolar electrode pairs and a second set of bipolar electrode pairs, the first and second sets of bipolar electrode pairs respectively connected to the implant device with first and second multi-contact connectors each having a pair of contacts associated with each bipolar electrode pair, and the implant device includes headers with first and second ports configured to receive the first and second multi-contact connectors, respectively.

20. 20. The method of claim 19, wherein the first and second ports include Bal Seal connectors configured to seal the implant device 20 when the first and second multi-contact connectors are inserted into the first and second ports, respectively.

21. The prosthetic device includes at least one pressure sensor, and the method further comprises: receiving at least one pressure signal from the at least one pressure sensor at the prosthetic device controller; communicating the pressure signal to the implant device with the prosthetic controller; receiving the pressure signal at the processing circuitry of the implant device; generating at least one stimulation signal in the processing circuitry of the implant device and transmitting it to at least one of the plurality of bipolar electrode pairs to stimulate the nerve of the subject through at least one of the free tissue grafts affixed to the nerve of the subject; The method of claim 13 further comprising:

22. 22. The method of claim 21, wherein the processing circuitry of the implant device is configured to alternate between the receiving of the electrical signal and the generating and transmitting of the at least one stimulation signal during consecutive time periods.

23. generating, at the processing circuitry of the implant device, commands for controlling the prosthetic device based on the received electrical signals; generating, at the processing circuitry of the implant device, estimated commands for controlling the prosthetic device during the time that the at least one stimulation signal is generated and transmitted to the at least one of the plurality of bipolar electrode pairs; 23. The method of claim 22, further comprising:

24. 22. The method of claim 21 , wherein the processing circuitry is configured to estimate an artifact in the electrical signal produced by the at least one stimulation signal and to subtract the artifact from the electrical signal.

25. 1. A computing device having a processing circuit configured to receive electrical signals from a plurality of free tissue grafts surgically affixed to a nerve of a subject, the electrical signals being received through a plurality of pairs of bipolar electrodes, each having a positive electrode and a negative electrode, implanted inside the plurality of free tissue grafts and in electrical communication with the plurality of free tissue grafts, the plurality of free tissue grafts being surgically affixed to the subject such that the plurality of free tissue grafts are completely surrounded by and in direct contact with non-implanted tissue of the subject, the plurality of free tissue grafts being tissue autografts that are removed from the subject, devascularized, denervated, and then surgically affixed to the subject, the processing circuit being further configured to process the received electrical signals, generate processed signal data, and transmit the processed signal data to a prosthetic controller. Equipped with the portion of the nerve being reinnervated from the plurality of free tissue grafts after the plurality of free tissue grafts have been surgically attached to the nerve; the electrical signals from the plurality of free tissue grafts have a voltage amplitude of about 150 microvolts or greater; the prosthetic controller is configured to control a prosthetic device based on the processed signal data transmitted from the processing circuitry of the computing device; A system wherein the multiple bipolar electrode pairs are connected to electrical leads extending outside the subject and connected to the computing device placed outside the subject, the computing device being in communication with the prosthetic device controller.

26. 26. The system of claim 25, wherein the processing circuitry is configured to apply a bandpass filter to the received electrical signal and to sample the filtered electrical signal.

27. 27. The system of claim 26, wherein the bandpass filter is a 100 to 500 Hz bandpass filter, and the processing circuitry is configured to sample the filtered electrical signal at a sampling rate of 1 kHz.

28. 28. The system of claim 27, wherein the processing circuitry is further configured to generate the processed signal data by calculating a mean absolute value of the sampled electrical signals, and to wirelessly transmit the mean absolute value of the sampled electrical signals as the processed signal data to the prosthetic device controller.

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