Contextual de-coding for brain-computer interface systems

Abstract

A decoder for a brain-computer interface (BCI) that contextually decodes neural signals from an individual using the BCI using contextual data, and translates them into certain executable commands, enabling the BCI to interact with a device coupled to the BCI.

Description

[0001] Cross-references to related applications This application is a provisional application filed on October 29, 2023, under U.S. Application No. 63 / 594,022, the entire contents of which are incorporated herein by reference. Background of the Invention Decoders used in brain-computer interfaces (BCIs) detect individual neural signals recorded or detected using the BCI and decode these signals into an operable command, allowing the BCI to interact with devices coupled to it. Some decoders are more suitable than others in certain situations. BCI decoders are often used to change the state of computer applications. Some state transitions have higher potential functionality than others and bring corresponding usage consequences (e.g., pressing send on an email compared to typing characters). Changing the decoder based on its accuracy / speed and the current context of the application state may be appropriate. However, for individuals, having to manually switch decoders is cumbersome and undermines autonomy. Further improvements to BCI interfaces and modifications to the decoder portion of the system are still needed to increase ease of use and individual benefits. Invention Overview This disclosure includes a method for decoding electronic signals generated by a neural interface device configured to detect individual brain activity, and using contextual information to determine how to decode the signals such that the decoding of the signals is influenced by individual-related factors.

[0004] In one variant, the method includes: transmitting an electronic signal to a computer processor; generating contextual data by actively monitoring an individual; processing the electronic signal using the computer processor to produce an output signal, wherein the computer processor is configured to selectively apply at least one of a plurality of algorithms to decode the electronic signal, wherein the selection of at least one algorithm depends at least in part on the contextual data; and electronically transmitting the output signal to one or more external electronic devices, enabling the individual to interact with one or more external electronic devices using brain activity.

[0005] In another variation, the method described herein includes: facilitating interaction between an individual and one or more electronic devices using individual-related contextual information when an individual uses a neural interface device configured to generate electronic signals decoded from the individual's brain activity, the method comprising: transmitting the electronic signals to a computer processor; generating contextual input data by monitoring individual-related contextual information; processing the electronic signals using the computer processor to selectively apply at least one of a plurality of algorithms to decode the electronic signals to produce an output signal, wherein the selection of at least one algorithm depends at least in part on the contextual input data; and electronically transmitting the output signal to one or more electronic devices, enabling the individual to interact with one or more electronic devices using brain activity.

[0006] Contextual data can be generated by monitoring an individual before or during interactions with one or more external electronic devices. Monitoring of the individual can be proactive, by observing real-time conditions relevant to the individual as they interact with the BCI and the various electronic devices coupled thereto. Alternatively or in combination, monitoring of the individual can be performed by monitoring the individual's conditional history as they interact with the BCI and the various electronic devices coupled thereto. Contextual data may include information about the electronic devices among the multiple external electronic devices actively engaged by the individual and / or about which of the multiple external electronic devices are actively coupled to the computer processor.

[0007] In another variation, the computer processor confirms that the electronic signal represents brain activity intentionally generated by the individual before processing it.

[0008] As described in this article, generating contextual data by monitoring individuals includes acquiring data on: environmental factors relevant to the individual, health information relevant to the individual, how the individual uses one or more external electronic devices, whether the individual attempts to move the cursor on one or more external electronic devices, and whether the individual attempts to electronically type text on one or more external electronic devices.

[0009] The results of contextual data allow for the selection of algorithms that reduce output signal latency, increase output signal latency, increase output signal accuracy, and / or increase or decrease the speed at which output signals are generated. Increasing latency will slow down the interaction between the individual and the control device of the BCI. Reducing latency can improve the speed of interaction. If a user attempts to implement increased control over the interface (e.g., typing) or needs to reduce control (e.g., scrolling text), the accuracy of the increased and / or decreased output can be adjusted.

[0010] Contextual data can also select an algorithm that produces a continuous output signal (e.g., if a user tries to move the cursor on the screen) or an algorithm that produces a discrete output signal (e.g., when selecting a button or link).

[0011] Variations of this disclosure include a method in which a computer processor is located within a signal control unit, the signal control unit including a housing structure physically separated from and configured as portable from a neural interface device, and wherein electronically transmitting an output signal to one or more external electronic devices includes electronically transmitting the output signal from the signal control unit.

[0012] Generating contextual data by monitoring an individual includes acquiring data about which of one or more external electronic devices is operatively connected to the signal control unit, and / or monitoring an individual includes acquiring data about various electronic communication modes operatively connected to the signal control unit. Monitoring can be proactive, by monitoring what the individual is currently doing, and / or monitoring can include information about the individual and / or components of the system that the individual can interact with using the BCI system. Generating contextual data may also include proactively monitoring the individual to acquire data about the activity status (sleep, active, low energy, etc.) of the signal control unit, or by monitoring previously output signals transmitted by the individual to one or more external electronic devices. Brief description of the attached diagram The accompanying drawings shown and described are exemplary embodiments and are not limiting. The same reference numerals always indicate the same or functionally equivalent features.

[0014] Figure 1A This is a representative illustration of a brain-computer interface, including implanted / electrode devices located within an individual's brain.

[0015] Figure 1B A remote power source is shown that can be used for wireless charging and / or communication with implantable receiver and transmitter units.

[0016] Figure 1C The illustration shows a variation of the signal control unit, which houses the power supply, processor, and circuitry to enable wireless and / or wired electronic communication.

[0017] Figure 1D The illustration shows an example of a portable signal control unit with a housing that includes a user interface.

[0018] Figure 1E illustrates an embodiment of a brain-computer interface (BCI) system.

[0019] Figure 1F illustrates one embodiment of a stent electrode array implanted within an individual's cerebral blood vessels. The stent electrode array can be an example of a recording device in a BCI system.

[0020] Figure 1G illustrates a communication conduit that connects the support electrode array to the receiver and transmitter units of the BCI system.

[0021] Figure 1H The illustration shows a close-up view of an embodiment of the receiver and transmitter unit of a BCI system.

[0022] Figure 1I The diagram illustrates an additional variant of the interface system in which multiple different communication channels of the signal control unit allow individual BCI users to operatively enable one or more external devices to enhance individual autonomy.

[0023] Figure 2A An embodiment of a coiled wire carrying multiple electrodes is illustrated. The coiled wire can be another example of a recording device in a BCI system.

[0024] Figure 2B An embodiment of an anchoring conductor carrying multiple electrodes is illustrated. The anchoring conductor can be another example of a recording device in a BCI system.

[0025] Figure 2C An embodiment of an electroencephalogram (EEG) device used as a recording device in a BCI system is illustrated.

[0026] Figure 2D An embodiment of an electrocorticography (ECoG) device used as a recording device in a BCI system is illustrated.

[0027] Figure 2E An embodiment of a functional magnetic resonance imaging (fMRI) device used as a recording device in a BCI system is illustrated.

[0028] Figure 2F An embodiment of a functional near-infrared spectroscopy (fNIRS) device used as a recording device in a BCI system is illustrated.

[0029] Figure 3A The illustration shows certain software layers or modules running on the computing devices of a BCI system.

[0030] Figure 3B The illustration shows a basic example of how to determine which decoder is used to decode signals in a BCI.

[0031] Figure 4A An embodiment of a neurofeedback graphical user interface (GUI) is illustrated.

[0032] Figure 4B Another embodiment of the neurofeedback GUI is illustrated.

[0033] Figure 4CAnother embodiment of the neurofeedback GUI is illustrated.

[0034] Figure 4D An additional embodiment of the neurofeedback GUI is illustrated.

[0035] Figure 4E Another embodiment of the neurofeedback GUI is illustrated.

[0036] Figure 5A The illustration shows an example of graphical elements representing an individual's current brain activity.

[0037] Figure 5B The illustration shows another embodiment of graphical elements representing an individual's current brain activity.

[0038] Figure 5C The illustration shows yet another embodiment of graphical elements representing an individual's current brain activity.

[0039] Figure 6 An embodiment of a neural feedback GUI that includes graphical elements displayed sequentially over time is illustrated.

[0040] Figure 7 Examples of different types of sounds that can be generated by an auditory feedback component are illustrated.

[0041] Figure 8 Examples of different types of haptic feedback components are illustrated, which can generate haptic feedback that can be felt by an individual.

[0042] Detailed description Figure 1A This is a representative illustration of a brain-computer interface (BCI) including an implantable / electrode device 100 located within the brain 12 of an individual. The implant 100 is coupled to a receiver and transmitter unit 102, which generates electrical neural signals corresponding to brain signals detected by the implant 100 in the individual's brain. Typically, the implant 100 is coupled to the receiver and transmitter unit 102 via one or more leads 104. However, this communication can be wireless. Furthermore, the system described herein can be used with a variety of other implantable and non-implantable external devices configured to detect brain activity. Additionally, the receiver and transmitter unit 102 may include an implantable housing where charging is performed via an external charging unit in a capacitor or similar configuration. Alternative variations include a receiver and transmitter unit 102 located externally to the individual 10.

[0043] Figure 1AA receiver and transmitter unit 102 is also shown, which has the capability to communicate with a signal control unit 120. This signal control unit receives transmissions from the receiver and transmitter unit 102 and is configured to perform signal processing on electronic neural signals to perform any number of functions that interact with host device 130 or multiple host devices. Host devices may include any electronic device, such as a computer or tablet, including dedicated applications, and may also include non-dedicated (proprietary and / or non-proprietary) applications. The host device may support secure and proprietary communication with the signal control unit 120 and may provide a user interface for individual BCI users. In some variations, the individual uses host device 130 to control a wide variety of applications. In another variation, the receiver and transmitter functionality 102 and the signal control functionality 120 may be housed in the same hardware, implanted within the individual.

[0044] The signal processing may include filtering, classifying, decoding, and transmitting data received from the receiver and transmitter units. In one variant, the system of the present invention includes only a signal control unit 120 and one or more external host devices 130, in which case the signal control unit 120 operates in conjunction with various systems. One advantage of using a dedicated signal control unit 120 is that it provides a signal control unit 120 that allows a power-efficient, low-latency device to interact with one or more electronic devices. Additionally, most signal processing and data storage can occur within the signal control unit 120. The signal control unit 120 can be dedicated to signal processing and decision-making and equipped with customized applications to guide user interaction. In another variant, the signal control unit 120 is configured to access a cloud network 150 for computing and storage resources or for analysis. By offsetting these requirements from implantable components, the weight and size of the transmitter unit 120 are minimized, and the heat generated by the transmitter unit 102 during operation is reduced.

[0045] Furthermore, transferring all or most of the computing power to the signal control unit 120 allows any software updates to be performed outside the BCI user body. Additionally, this configuration allows the receiver and transmitter units 102 to operate with lower power requirements, reducing the frequency of recharging. In alternative variations, processing, storage, and communication functions can be distributed among the receiver and transmitter units 102, the signal control unit 120, and / or any external devices 130.

[0046] Typically, a BCI system (implant 100 and receiver and transmitter unit 102) captures motor intentions from the brain (e.g., the motor cortex) and generates one or more electrical signals corresponding to those intentions. Electrical signals can be captured from brain activity in areas other than the motor cortex. A signal control unit 120 decodes the electrical signals for use with a host device 130 to control software applications (typically located on the host device 130). In some cases, the host device 130 can be used to control additional digital devices (such as computers, wheelchairs, home automation systems, or other devices) that assist the individual 10 in using the BCI.

[0047] Variations of the systems and methods described herein include the advantage of increasing the lifespan of the implanted components (e.g., 100, 104, 102) to avoid repeated surgeries to replace the implanted components. Therefore, as... Figure 1B As shown, variations of the system and method allow the receiver and transmitter unit 102 to be remotely and / or wirelessly recharged using an external power source (e.g., capacitive charging). Alternative variations may include receiver and transmitter unit 102 that allows physical connection to an external power source.

[0048] In one example, the system is designed to increase lifespan and provide increased mobility and autonomy to BCI user 10. Such systems and methods can employ an architecture that distributes processing and data storage capabilities across non-implantable components of the system, where an implanted receiver and transmitter unit 102 is responsible for acquiring and transmitting signals indicative of the user's intent. For example, the receiver and transmitter unit 102 can communicate with electrodes 100 such that when the electrodes detect brain signals from the brain of BCI user 10, the receiver and transmitter unit is configured to transmit electronic signals representing those brain signals.

[0049] Figure 1C A variation of the signal control unit 120 is shown, which houses a power supply 50, a processor 52, and circuitry 54-62 for enabling wireless and / or wired electronic communication. The power supply 50 may include a battery or an external power source. The processor 52 is configured to apply one or more algorithms to decode electronic signals from a transmission component. The signal control unit 120 may also be configured to generate an output signal upon determining that the electronic signal represents an intentional neural brain signal generated by an individual. Therefore, the signal control unit 120 can house any number of communication circuitry or modules 54, 56, 58, 60, and 62, which can be used for electronic communication with one or more external devices, as described below. These modules can provide the signal control unit 120 with wireless or wired communication capabilities. Examples of wireless communication include, but are not limited to, short-range wireless (e.g., Bluetooth or Bluetooth Low Energy), ultra-high frequency (e.g., 433MHz low-power devices), and the ability to communicate via HTTP / HTTPS using Wi-Fi or other network connections. Figure 1C In the variant shown, the signal control unit 120 may include one or more Bluetooth Low Energy (BLE) modules to communicate simultaneously or sequentially with the receiver and transmitter unit 120 and any number of external devices 130, 132. The signal control unit 120 may also include different modules to communicate simultaneously with the alarm device 128. Although not shown, the signal control unit 120 may include one or more speakers or alarms to play tones, a series of tones, pre-recorded messages, or other audible messages / sounds. In another variant, one or more modules may include sensors to provide environmental and / or physiological information about an individual. Examples of environmental information may include an individual's movement (using an accelerometer or GPS), ambient temperature, noise, etc. Examples of the user's physiological data may include heart rate, fatigue, caffeine, body temperature, blood pressure, etc.

[0050] In one variant of the system, the system is configured such that the signal control unit 120 is configured to interact with the receiver and transmitter unit 102 using a specific communication mode to restrict the distribution of data from the receiver and transmitter unit 102. This specific communication mode can be encrypted, secure, and / or otherwise proprietary. This prevents data transmission from the receiver and transmitter unit 102 to unauthorized devices. In some variants of the system, the host device is configured to receive data from the signal control unit 120 using this specific communication mode. Therefore, one difference between the host device 130 and other external devices 132 is that the external devices receive data using a standard communication mode. In one variant of the system, the specific communication mode may include proprietary and / or encrypted BLE, while communication with other external devices depends on other communication modes. This allows the signal control unit 120 to isolate / control various other communication modes to prevent accidental data output (e.g., to prevent unintentional data transmission over the Internet or to external devices).

[0051] In another variation of the system, the signal control unit 120 receives electronic signals from the receiver and transmitter unit 102 and generates a decoded output signal. The signal control unit 120 knows which external devices are connected and active. Based on this information, the signal control unit 120 can generate an output signal for HID. This HID output signal can be sent to the currently active terminal device. In another variation of the system, multiple external devices can connect to the signal control unit 120, but the signal control unit 120 is configured to send an output signal to only one device at a time. While terminal devices can be paired with the host device 130 (e.g., by a caregiver), the user can use neural signals to control which device is active. Pairing refers to the process of enabling two electronic devices to establish a connection so they can communicate directly with each other, for example via Bluetooth wireless technology, and typically includes authentication steps to ensure the connection is secure. Additionally, during an active HID session with a terminal device, another active session with the host device can be conducted using a different wireless protocol, allowing secure input / output from the signal control unit 120 to the host device, which informs the signal control unit 120 of its configuration and control. Alternatively, a third "proto-profile" or a different wireless signal based on a third wireless protocol can utilize input and output signals communicating with the host device's operating system (e.g., iOS toggle controls or AssistiveTouch) to allow an individual to control the desktop and any app of the host device or "terminal" device based on contextual data from the host device, rather than the host device itself. By utilizing multiple different wireless profiles (protocols or modes), the signal control unit 120 can adaptively communicate with multiple connected devices based on decoded signals and the connected devices with which the signal control unit 120 is communicating.

[0052] Figure 1D An example of a portable signal control unit 120 with a housing 168 is shown, featuring a minimal user interface on the housing to achieve the low-power design described herein. The housing 168 is chosen to be portable (e.g., it can be placed in a clothing pocket or worn by an individual). Figure 1DThe user interface shown uses auditory, haptic, and / or optical feedback to provide information to the user. Housing 168 includes a power button 170 that can also toggle the signal control unit 120 between locked and unlocked configurations and sleep / low-power modes. The power button 170 can be elastomeric or capacitive. An area 172 surrounding the power button can provide general information. In the example shown, the information area 172 displays a ring-shaped light indicator to convey certain functions of the signal control unit 120. This light can vary in size and color and can pulsate using different presentation frequencies to convey information. Housing 168 may also include a notification indicator 174, which can be used to draw attention to system information such as errors or other notifications. Housing 168 may also include a power indicator 176 to display the power level / state of charge. Additionally, housing 168 may include a connection indicator to convey the connection status of the signal control unit 120 to receiver and transmitter units or other components of the system. Variations of the signal control unit 120 may include any combination of the above-described indicators. Furthermore, in another variation, the signal control unit 120 may include a detailed user interface rather than a limited one. Although not shown, the signal control unit 120 may include any additional ports, a reset button, a speaker, etc.

[0053] A limited user interface with portable / pocket-sized hardware provides BCI users with prosthetic hardware and functionality to replace at least some of the lost mobility and function of the peripheral nervous system. To reduce power consumption between the user interaction host and terminal devices, the signal control unit 120 can be configured to disconnect any external devices (e.g., the host and / or external devices) when the receiver and transmitter unit 102 are in idle mode to save power. The signal control unit 120 can also automatically or quickly turn on the host device upon request. The ability to monitor connected devices and selectively enable various communication modes allows the signal control unit 120 to provide users with BCI functionality for at least 4, 8, 12, or 24 hours on a single battery charge, during which time the signal control unit 120 can receive, decode output signals, and transmit different output signals to multiple external devices without external power. Therefore, the signal control unit 120 can use information about the individual, including information about the components 130 that the individual interacts with, and use such information to improve the decoding of neural signals, as described below.

[0054] Figure 1E illustrates another variant of a brain-computer interface (BCI) system used by individuals with mobility limitations to control peripheral devices such as personal electronic devices, Internet of Things (IoT) devices, or mobile vehicles, or software applications running on such peripheral devices. An effective BCI system should allow individuals across the entire spectrum of mobility limitations to effectively control such peripheral devices or software applications, including those with severe mobility limitations, such as those with locked-in syndrome. Individuals control the BCI system by modulating their brain activity, which is monitored by one or more components of the BCI system.

[0055] The BCI system may include a recording device or implant 100 (see Figure 11F) and a computing device 130. Alternatively, as described above, a variant of the system may use a signal control unit. The recording device 100 may be configured to detect brain activity of the individual 10. The variant shown illustrates the recording device 100 being configured as a scaffold 108 having multiple electrodes 103 implanted within a cerebral blood vessel 104 of the individual. The implant 100 may be implanted within the cortex or cerebral veins or sinuses of the individual.

[0056] However, alternative configurations are available within the scope of this disclosure, such as recording devices external to the body, and devices placed directly on or within brain tissue, or placed on top of brain tissue under the dura mater. In other embodiments, the recording device 100 may be a non-invasive recording device, such as an electroencephalogram (EEG) device (see, for example...). Figure 2C ), functional magnetic resonance imaging (fMRI) equipment (see, for example) Figure 2E ) or functional near-infrared spectroscopy (fNIRS) equipment (see, for example) Figure 2F ).

[0057] In other embodiments, the stent electrode array 102 may be any stent, stent structure, stent electrode, or stent electrode array disclosed in the following: U.S. Patent Publication No. 2021 / 0365117, U.S. Patent Publication No. 2021 / 0361950, U.S. Patent Publication No. 2020 / 0363869, U.S. Patent Publication No. 2020 / 0078195, U.S. Patent Publication No. 2020 / 0016396, U.S. Patent Publication No. 2019 / 0336748, U.S. Patent Publication No. US U.S. Patent No. 10,575,783, 10,485,968, 10,729,530, 10,512,555, U.S. Patent Application No. 62 / 927,574 (filed October 29, 2019), U.S. Patent Application No. 62 / 932,906 (filed November 8, 2019), and U.S. Patent Application No. 62 / 932,935 (filed November 8, 2019) are all related to patent applications filed in the United States. The entire contents of the following patents are incorporated herein by reference: U.S. Patent Application No. 62 / 935,901, filed November 15, 2019; U.S. Patent Application No. 62 / 941,317, filed November 27, 2019; U.S. Patent Application No. 62 / 950,629, filed December 19, 2019; U.S. Patent Application No. 63 / 003,480, filed April 1, 2020; and U.S. Patent Application No. 63 / 057,379, filed July 28, 2020.

[0058] When a recording device 100 (e.g., a stent electrode array 102) is implanted within a blood vessel 104 in an individual's brain, each electrode 103 of the recording device 100 can be configured to read or record the electrical activity of neurons in the vicinity of the electrode 103. Neuronal electrical activity is typically recorded as rhythmic or repetitive activity patterns, also known as neural oscillations or brain waves. Such neural oscillations or brain waves can be further subdivided into frequency bands based on their frequency. For example, rhythmic neuronal activity between 14 Hz and 30 Hz is referred to as neuronal oscillations in the β frequency range or β band.

[0059] When a recording device 100 (e.g., the stent electrode array 100 of FIG. 1F) is implanted in a brain vessel 14 of an individual, the device 100 detects any changes over time in the individual's neural oscillations, including the β band (approximately 14 Hz to 30 Hz), the α frequency range or band (approximately 7 Hz to 12 Hz), the θ frequency range or band (approximately 4 Hz to 7 Hz), the γ frequency range or band including a low-frequency γ band (approximately 30 Hz to 70 Hz) and a high-frequency γ band (approximately 70 Hz to 135 Hz), the δ frequency range or band (approximately 0.1 Hz to 3 Hz), the μ frequency range or band (approximately 7.5 Hz to 12.5 Hz), the sensorimotor rhythm (SMR) frequency range or band (approximately 12.5 Hz to 15.5 Hz), or combinations thereof. Device 100 can record the power variation of this neural oscillation (e.g., measured in decibels (dB), microvolt squares per Hz (μV2 / Hz), average t fraction, average z fraction, etc.).

[0060] In some embodiments, the device 100 may be implanted within a brain or cortical vein or sinus, or directly onto or within brain tissue, or placed on top of brain tissue beneath the dura mater. For example, the recording device 100 may be implanted within the superior sagittal sinus, inferior sagittal sinus, sigmoid sinus, transverse sinus, straight sinus, superficial cerebral veins (such as the Labbe vein, Trolard vein, Sylvian vein, Rolandic vein), deep cerebral veins (such as the Rosenthal vein, Galen vein, superior thalamic-striatal vein, hypothalamic-striatal vein), or internal cerebral veins, the central sulcus vein, the posterior central sulcus vein, or the anterior central sulcus vein. In some embodiments, the recording device 100 may be implanted within a blood vessel extending through the hippocampus or amygdala of an individual.

[0061] Figure 1G illustrates a communication conduit or lead 104 (e.g., a lead) that allows the recording device 100 to connect with the receiver and transmitter unit 102. Figure 1H The unit is communicatively coupled to the computing device 130 or signal control unit as described above. Alternatively, the lead 104 can directly connect the recording device 100 to any external electronic device (e.g., the computing device 130).

[0062] Lead 104 may be a biocompatible wire or cable. When device 100 is a stent electrode array 102 deployed within an individual's cerebral blood vessels (e.g., superior sagittal sinus) 14, lead 104 may extend through one or more cerebral blood vessels and through the individual's venous walls. Lead 104 may be positioned subcutaneously to reach a region of the individual (e.g., below the pectoralis major muscle) where receiver and transmitter units 102 are implanted.

[0063] Figure 1H A close-up view of an embodiment of the receiver and transmitter unit 102 is shown. In some embodiments, the receiver and transmitter unit 102 may be configured to transmit signals received from the recording device 100 to the computing device 130 for processing and analysis. The receiver and transmitter unit 102 may also serve as a communication hub between the recording device 100 and the computing device 130. In some embodiments, the computing device 130 may transmit commands or signals to the receiver and transmitter unit 102 to generate certain user outputs. Generating user outputs to train individuals to better control the BCI system 100 will be discussed in more detail in later sections.

[0064] In some embodiments, the receiver and transmitter unit 102 may be an internal receiver and transmitter unit 102 that can be implanted under the skin of an individual. For example, the receiver and transmitter unit 102 may be implanted in the chest region or subclavian space of an individual. In other variations, the receiver and transmitter unit may be implanted anywhere in the body (e.g., within the skull) or may be located wholly or partially outside the body.

[0065] In other embodiments, the receiver and transmitter unit 102 may be an external receiver and transmitter unit 102 not implanted within the individual. In these embodiments, the lead 104 may extend through the individual's skin to connect to the receiver and transmitter unit 102. In yet another embodiment, the receiver and transmitter unit 102 may include both an implantable portion and an external portion.

[0066] In some embodiments, the receiver and transmitter unit 102 may transmit data or signals to or receive data or commands from the computing device 130 via a wired connection. In other embodiments, the receiver and transmitter unit 102 may transmit data or signals to or receive data or commands from the computing device 130 via wireless communication protocols such as Bluetooth™, Bluetooth Low Energy (BLE), ZigBee™, WiFi, or combinations thereof, as described above.

[0067] Figure 1IAnother variation is shown in which the BCI (device 100, lead 104, and receiver and transmitter unit 102 coupled to an individual BCI user) is configured to use multiple different communication channels of signal control unit 120 to allow the individual BCI user to operatively enable one or more external devices to enhance individual autonomy. The neural interface device 100 detects brain signals or neural activity from the individual and is electrically coupled to receiver and transmitter unit / component 102. Upon receiving a signal from electrode component 100, the receiver and transmitter component transmits an electronic signal representing the brain signal to signal control unit 120. In one variation, receiver and transmitter unit 102 uses BLE transmission 22 to transmit an electronic signal representing the brain signal to signal control unit 120. As described herein, the use of BLE allows for a reduction in power requirements from receiver and transmitter unit 102. However, additional variations of the systems and methods disclosed herein can use any wireless transmission mode as discussed herein. Figure 1I The system shown is further discussed in U.S. Patent Application No. 18 / 882,591, which is incorporated herein by reference in its entirety.

[0068] It should be noted that Figure 1I Various specific wireless transmission modes between different components are illustrated. Examples include BLE 22, Network / Internet / HTTPS 24, UHF 26, and Short Message Service “SMS” 28. These specific wireless transmission modes demonstrate a variant of an improved BCI device (100, 104, 102, 120, 130) operating in a larger system. However, additional system configurations consider any wireless transmission mode used between any components of the system.

[0069] Figure 1I The system variant shown includes a signal control unit 120 having a housing structure physically separate from the neural interface devices (100, 102, 104) and configured to be portable, allowing the signal control unit 120 to remain with the individual while maintaining operational engagement with the system. The signal control unit 120 uses one or more processors to process electronic signals representing brain signals, applying one or more algorithms to decode the electronic signals from the transmission components. As described above, the system variant includes performing all or most of the electronic signal processing within the signal control unit 120. However, additional variants may include performing signal processing within one or more processors in the receiver and transmitter unit 102. Alternatively or in combination, some processing may occur externally from the host device 130 or via one or more cloud-based networks 150.

[0070] Once the system determines that the electronic signal represents an intentional neural brain signal generated by the individual, the signal control unit 120 can use the signal control unit to transmit the output signal to one or more external devices. In the illustrated variant, the signal control unit 120 exchanges data with a personal host device 130 (such as a tablet computer, computer, or other electronic device) via BLE transmission 22. However, additional variants of the system may include a signal control unit 120 that communicates directly with other external electronic devices 160, 162, 164.

[0071] In some variations, the signal control unit 120 is configured to work with one or more terminal devices 132, where the terminal devices are any digital devices that support HID profiles for keyboards, mice, or other peripherals. Interconnection with the terminal devices in an individual's home can be facilitated by the host device 130, connecting to the BLE HID device via traditional key pairing. In one example, the terminal device may include an eye tracker or other applications that help an individual use the BCI interface, or an advanced mixed reality headset that enables a "space computer" that merges digital content with the physical environment, such as the Apple Vision Pro and Meta's Quest Pro and Quest 3.

[0072] System variants include a host device 130 that supports secure and proprietary communication with the signal control unit 120 and provides the necessary user interface to support individual training and use of the entire BCI system. Individuals can use their host devices to control a wide variety of applications, including but not limited to sending text messages, writing documents, using social media, internet communication, shopping, interacting with home appliances and home automation, health applications, banking applications, etc.

[0073] Figure 1I The system shown also envisions one or more external electronic devices 130, 160, 162, and 164 generating or transmitting data (“device data”). This device data is transmitted to a signal control unit 120, which can then transmit the device data back to any of the external electronic devices 130, 160, 162, and 164.

[0074] Figure 1IThe interface system is shown to interact with cloud-based software and data via various communication paths. In one variant, various host devices 130 can synchronize data with the cloud-based network 150 for multiple purposes. The cloud network 150 can also store proprietary software associated with system components, as well as authentication and certificate services, which are used during product use and in the production, manufacturing, and installation of the system. Internet connection 24 (between signal control unit 120 and cloud 150) can also be used to generate online notifications to caregivers. In the example shown, signal control unit 120 can send data to cloud 150, which ultimately delivers it to the caregiver's mobile phone 28 via SMS 154. Similarly, caregivers can communicate with the user's host device 130 via SMS 154 or directly with signal control unit 120 via internet connection.

[0075] The system network and communication channels allow various individuals to connect to the cloud to monitor and review the performance of BCI users, thereby providing assistance or improving system performance. In some variants, the cloud network 150 stores neural data from individuals (or various other individuals) and can forward requests to third-party services, including support for notification use cases described in more detail elsewhere in this document.

[0076] Cloud Network 150 also allows the system to access artificial intelligence (AI) that can be transmitted to any component of the system. In the example shown, the AI ​​involves a large language model 152 that helps BCI users communicate with others by providing generated content, as described in U.S. Application No. 18 / 734,476, filed June 5, 2024, the entire contents of which are incorporated herein by reference.

[0077] An additional benefit of the interface system and method described herein is the "always-on" functionality provided by low power consumption, portability, and a variety of different wireless communication modes. For example, given that the receiver and transmitter units 102 are charged and the signal control unit 120 and host device 130 are powered, an individual can use the entire system independently and on demand for extended periods (e.g., 24 hours or more). This always-on functionality enables individuals to engage in digital daily life activities such as telemedicine, social media, communication, or adapting to various home interface systems based on automation or appliance controls (e.g., 160, 162, 164). More importantly, the always-on functionality can provide assistance by providing potentially life-saving messages to caregivers. By being able to operate the system for extended periods on battery power, this notification function allows it to function outdoors, away from the home, and typically without internet access. Even without an internet connection, the system can still communicate with other devices in the individual's home (160, 162, 164) or devices outside the home. If the host device 130 is offline, the signal control unit 120 can also communicate with devices. For example, the signal control unit 120 can use a 433 MHz low-power device to send signals to one or more alarm devices 128. The always-on function also allows the user to switch from an idle state and immediately request assistance from caregivers (e.g., if an individual wakes up at night and can immediately message a caregiver).

[0078] Another benefit of the described system includes selective control between the host 130 and any number of external devices 160, 162, 164, wherein control is adaptive based on the binding and / or active connection between the control unit 132 device and the external devices 160, 162, 164, and based on differentiated intent information (confirmation of intentional neural brain signals generated by the individual and decoded into electronic signals transmitted to the SCU). An active connection means that the external device is bound to the host 130 and is in an active state, making the external device ready to exchange data with the host.

[0079] Adaptive control can occur with the host device 120 and any number of external devices selected by the user using the signal control unit. The signal control unit 120 learns which devices are connected through the binding process described above, and adaptively modifies the decoded intent signals generated by each device accordingly. The term "binding" is intended to refer to a relationship established between two devices that allows for secure reconnection without re-pairing. This can be accomplished by the host device or any device in the system. In some cases, the term "pairing" includes any process by which devices exchange information necessary to establish or encrypt a connection.

[0080] When the signal control unit 120 converts a decoded signal into one or more output signals, it can use information about the number of bound devices to consider how to relay the output signals. This allows individual BCI users to autonomously control their interactions with various external devices without relying on caregivers. As an example, as described above, if an individual intends to contact caregivers, once this intention is sent to the signal control unit for decoding and confirmation, the signal control unit 120 can generate an output command based on the available bound devices. If the BCI user is without bound devices or without network connectivity, the signal control unit 120 can transmit the output via a fallback communication protocol (e.g., 433 MHz, transmitted to alarm device 128). This capability can also be applied to system warnings.

[0081] In one variant, the BCI system can be configured to monitor the system's ability to detect brain signals from the user and determine whether the associated electronic signals represent intentional neural brain signals generated by the individual. In some cases, an individual may experience a deterioration in health, causing changes or degradation in the signal transmission generated by the brain, resulting in the previously functioning BCI system no longer operating properly due to the individual's worsening condition. In this situation, the signal control unit 120 can be configured to provide notification to caregivers or other healthcare professionals.

[0082] exist Figure 1IIn another variation of the system shown, the signal control unit 120 receives electronic signals from the receiver and transmitter unit 102 and generates a decoded output signal. The signal control unit 120 understands which external devices are connected and active. Based on this information, the signal control unit 120 can generate an output signal for HID. This HID output signal can be sent to the currently active terminal device. In another variation of the system, multiple external devices can be connected to the signal control unit 120, but the signal control unit 120 is configured to send an output signal to only one device at a time. While terminal devices can be paired with the host device 130 (e.g., by a caregiver), the user can use neural signals to control which device is active. Additionally, during an active HID session with a terminal device, another active session with the host device can be conducted using a different wireless protocol, allowing secure input / output from the signal control unit 120 to the host device, which informs the signal control unit 120 of its configuration and control. Furthermore, or alternatively, a third “prototype profile” or a standalone wireless signal based on a third wireless protocol can utilize input and output signals communicating with the host device’s operating system (e.g., iOS toggle controls or AssistiveTouch) to allow an individual to control the desktop and any app on the host device or “terminal” device, rather than the host device, based on contextual data from the host device. By utilizing multiple different wireless profiles (protocols or modes), the signal control unit 120 can adaptively communicate with multiple connected devices based on decoded signals and the connected devices with which the signal control unit 120 is communicating.

[0083] In some variations of the system, the connection between the signal control unit 120 and the host device 130 is disconnected immediately after data transmission and when there is no data transmission between the signal control unit 120 and the host device 130 to save power. However, this may increase system latency. To improve the user experience of the system described in this disclosure, for example, as... Figure 3A and Figure 3BAs shown, system components can be configured to minimize system latency, allowing users to activate host / external devices using brain activity. For example, both the host and terminal devices can maintain their connection while the signal control unit 120 is in use. This enables low system latency (e.g., less than 100 ms) from the detection of neural signals, the transmission and wireless reception of signals by the signal control unit 120, the decoding and conversion of neural signals into output signals using the signal control unit 120, and the transmission of a second wireless transmission from the signal control unit 120 to one or more external devices. Selective control between the host and terminal devices can be adaptive based on the detected binding and differentiated intent information (from the decoded signal of the neural signals received from the electrode devices and decoded by the signal control unit 120).

[0084] Figure 1I This illustration only provides one representation of the various electronic devices and systems that allow interaction by an individual user of the BCI. As described above, implant 100, signal control unit 102, and receiver and transmitter unit 102 are implanted in the body or carried by the individual. Therefore, signal control unit 120 can monitor the individual's physiological data, data related to actions taken by the individual when accessing one or more devices, or the types of devices the individual can access using the system. Thus, as described below, the decoding algorithm for translating neural activity into executable electronic commands can dynamically and adaptively change based on these individual-related monitoring data / factors to produce different or altered outputs. This contextualized decoding aims to better assist the individual when using the BCI and allows the application of decoding algorithms best suited to the individual's ongoing activity. For example, these factors may include selecting certain decoding algorithms based on the type of electronic devices enabled by the system or the type of electronic devices the individual is currently using. Such data may be generated by external devices (e.g., 130, 148, 160, 162, 164) or by a processor (e.g., in signal control unit 120). Alternatively, such data may be generated by additional sensors monitoring the individual.

[0085] Figure 2A Another variation of the recording device 100 is shown as a helical coil lead 200 comprising multiple electrodes 103. The helical coil lead 200 can serve as an intravascular carrier for the electrodes 103 and can be used in blood vessels too small to accommodate the stent electrode array 102. The helical coil lead 200 can be a biocompatible lead or a microlead, configured to wind itself into a helical pattern or a basic helical pattern. The electrodes 103 can be arranged such that the electrodes 103 are distributed along the length of the helical coil lead 200. More specifically, the electrodes 103 can be attached, fixed, or otherwise coupled to different points along the length of the helical coil lead 200.

[0086] Electrodes 103 can be separated from each other such that no two electrodes 103 are within a predetermined separation distance (e.g., at least 10 μm, at least 100 μm, or at least 1.0 mm). In some embodiments, the lead wire 200 can be configured to automatically wind itself into a helical configuration (e.g., a spiral pattern) when the lead wire 200 is unwound from the delivery catheter. For example, the helical coil lead wire 200 can automatically acquire its helical configuration through shape memory when the delivery catheter or sheath is retracted. The helical configuration or shape can be a preset shape or a shape-memorized shape of the lead wire 200 before it is introduced into the delivery catheter. The preset or pre-trained shape can be larger than the diameter of the intended deployment or implantation vessel so that the radial force applied by the coil can hold or position the helical coil lead wire 200 in the appropriate location within the deployment or implantation vessel.

[0087] The conductor 200 may be made in part of a shape memory alloy, a shape memory polymer, or a combination thereof. For example, the conductor 200 may be made in part of nitinol (e.g., nitinol wire). The conductor 200 may also be made in part of stainless steel, gold, platinum, nickel, titanium, tungsten, aluminum, nickel-chromium alloys, gold-palladium-rhodium alloys, chromium-nickel-molybdenum alloys, iridium, rhodium, or a combination thereof.

[0088] Figure 2B Another variation of the recording device 100 is shown as an anchoring lead 202 comprising multiple electrodes 103. The anchoring lead 202 can serve as an intravascular carrier for the electrodes 103 and can be used in blood vessels that are too small to accommodate the helical coil lead 200 or the stent electrode array 102. The anchoring lead 202 may include biocompatible leads or microleads that are attached to or otherwise coupled to an anchor or another type of intravascular fixation mechanism. Figure 2B The diagram illustrates that the anchoring conductor 202 may include a barbed anchor 204, a radially expandable anchor 206, or a combination thereof (both the barbed anchor 204 and the radially expandable anchor 206 are...). Figure 2B (Seen in dashed or phantom lines). In some embodiments, the barbed anchor 204 may be positioned at the distal end of the anchoring wire 202. In other embodiments, the barbed anchor 204 may be positioned along one or more sides of the wire or microwire. The barbs of the barbed anchor 204 may secure or moor the anchoring wire 202 to the implantation site within the individual. The radially expandable anchor 206 may be a coiled or looped section of the wire or microwire. The size of the coil or loop may allow it to conform to the lumen of the blood vessel and expand against the lumen wall to secure the anchoring wire 202 to the implantation site within the blood vessel. For example, the size of the coil or loop may be larger than the diameter of the blood vessel to which it is intended to be deployed or implanted, so that the radial force applied by the coil or loop can secure or position the anchoring wire 202 in the appropriate location within the blood vessel.

[0089] The electrodes 103 of the anchor wire 202 can be distributed along the length of the anchor wire 202. More specifically, the electrodes 103 can be attached, fixed, or otherwise coupled to different points along the length of the anchor wire 202. The electrodes 103 can be separated from each other such that no two electrodes 103 are within a predetermined separation distance (e.g., at least 10 μm, at least 100 μm, or at least 1.0 mm). Although Figure 2B The anchoring conductor 202 shown has only one barbed anchor 204 and one radially expandable anchor 206. This disclosure contemplates that the anchoring conductor 202 may include multiple barbed anchors 204 and / or radially expandable anchors 206.

[0090] Figure 2C Another variation of the recording device 100 is shown, which may be a non-invasive device such as an electroencephalogram (EEG) device 208. The EEG device 208 may be a head-mounted EEG device. For example, the EEG device 208 may be an EEG cap or EEG goggles configured to be worn by an individual. The EEG device 208 may include a plurality of non-invasive electrodes 210 configured to contact the individual's scalp.

[0091] The brain activity detected by the EEG device 208 can be an individual's neural oscillations or brain waves, similar to those recorded by the recording device 100. For example, the EEG device 208 can record any variation over time of such neural oscillations, including the beta band (approximately 14 Hz to 30 Hz), the alpha frequency range or band (approximately 7 Hz to 12 Hz), the theta frequency range or band (approximately 4 Hz to 7 Hz), the gamma frequency range or band including a low-frequency gamma band (approximately 30 Hz to 70 Hz) and a high-frequency gamma band (approximately 70 Hz to 135 Hz), the delta frequency range or band (approximately 0.1 Hz to 3 Hz), the μ frequency range or band (approximately 7.5 Hz to 12.5 Hz), the sensorimotor rhythm (SMR) frequency range or band (approximately 12.5 Hz to 15.5 Hz), or combinations thereof. EEG device 208 can record changes in the power of such neural oscillations (e.g., measured in decibels (dB), microvolt squares per Hz (μV2 / Hz), average t-fraction, average z-fraction, etc.).

[0092] Figure 2D In another embodiment of system 100, the recording device 100 may be an electrocorticography (ECoG) device 212 (also known as an intracranial EEG device). The ECoG device 10 may be a flexible or stretchable electrode mesh or one or more electrode patches implanted or placed on the surface of an individual's brain. The electrode mesh or electrode patch may include a plurality of electrodes 214 respectively arranged on the mesh or patch.

[0093] The brain activity detected by the ECoG device 212 can be neural oscillations or brain waves of individual 14, similar to those recorded by the scaffold electrode array 102. For example, the ECoG device 212 can record any variation of such neural oscillations over time, including the beta band (approximately 14 Hz to 30 Hz), the alpha frequency range or band (approximately 7 Hz to 12 Hz), the theta frequency range or band (approximately 4 Hz to 7 Hz), the gamma frequency range or band including a low-frequency gamma band (approximately 30 Hz to 70 Hz) and a high-frequency gamma band (approximately 70 Hz to 135 Hz), the delta frequency range or band (approximately 0.1 Hz to 3 Hz), the μ frequency range or band (approximately 7.5 Hz to 12.5 Hz), the sensorimotor rhythm (SMR) frequency range or band (approximately 12.5 Hz to 15.5 Hz), or combinations thereof. The ECoG device 212 can record changes in the power of such neural oscillations (e.g., measured in decibels (dB), microvolt squares per Hz (μV2 / Hz), average t-fraction, average z-fraction, etc.).

[0094] Figure 2E Another variation of the recording apparatus using a functional magnetic resonance imaging (fMRI) machine 216 is shown. The fMRI machine 216 can detect changes in intracranial blood flow and oxygen levels during neural activity undertaken by individual 14 during neurofeedback training. These changes in blood flow and oxygen levels are an indirect result of the individual's neural activity.

[0095] In some embodiments, the fMRI machine 216 may use blood oxygen level-dependent (BOLD) contrast imaging to measure an individual's brain activity. For example, the brain activity of individual 14 may be represented as changes in the BOLD signal. In other embodiments, the fMRI machine 216 may use arterial spin labeling (ASL) instead of BOLD contrast imaging to measure an individual's brain activity.

[0096] Figure 2F The illustration shows that in another embodiment of system 100, the recording device may be a functional near-infrared spectroscopy (fNIRS) device 218. fNIRS device 218 can use near-infrared light (NIR) to measure intracerebral hemodynamic activity during neural activity during neurofeedback training. For example, fNIRS device 218 may include an fNIRS cap configured to be worn on the head of an individual. fNIRS device 218 may include multiple NIR light sources and detectors (referred to as optodes). fNIRS device 218 can measure hemodynamic activity by measuring changes in the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the cerebral cortex.

[0097] Figure 3A An example is shown in which device 100 records or detects neural activity, causing receiver and transmitter unit 102 or signal control unit to generate electronic signals representing the neural activity and transmit the electronic signals to one or more computing devices / computer processors. For example, the computer processor may include signal control unit 120, or may include external electronic computing device 130. These devices 120 / 130 may be programmed to translate brain activity into predictions about an individual's intention 408. As will be discussed in more detail later, an intention may be a thought generated by the individual or an attempt by the individual to move a part of their body (e.g., move the individual's left hand or left ankle). Furthermore, an intention may also be a thought generated by the individual or an attempt by the individual to achieve or maintain a state of neural rest. In these cases, the intention is not directly related to the individual focusing their attention or viewing a specific graphic presented on a display of a device such as a computing device.

[0098] Figure 3A An example of a software layer or module running on computing device 120 / 130 of a BCI system is illustrated. For example, one or more processors of computing device 120 / 130 may be programmed to execute software instructions, thereby constituting various software layers or modules. In other embodiments not shown in the figures but contemplated by this disclosure, any reference to computing device or computer processor may also refer to a control unit or controller embedded within receiver and transmitter unit 102. In further embodiments contemplated by this disclosure, any reference to computing device may also refer to a computing device or control unit / controller that is part of recording device 100 (e.g., when recording device 100 is an fMRI machine or fNIRS device).

[0099] The preprocessing layer 302 may include multiple software filters or filtering modules configured to filter and smooth the raw signal acquired from the recording device 100. For example, when the recording device 100 is an intravascular recording device (e.g., a stent electrode array 102) configured to be implanted within a blood vessel in an individual's brain, various electrodes 103 of the recording device 100 can be used to monitor the individual's brain activity. As a more specific example, the individual's brain activity may be sampled every 100 ms, such that 100 ms "blocks" or segments of the recorded raw neural signals can be passed to the preprocessing layer 302 for processing and smoothing.

[0100] Preprocessing layer 302 may first apply (1) a threshold filter to filter the original signal using certain thresholds. Then, preprocessing layer 302 may apply (2) a notch filter to perform, for example, 50 Hz notch filtering, and also apply (3) a bandpass filter to perform, for example, 4–30 Hz Butterworth bandpass filtering. Then, preprocessing layer 302 may apply (4) a wavelet artifact removal filter to perform wavelet-based artifact suppression, apply (5) a multi-cone spectral decomposition filter to perform multi-cone spectral decomposition, and apply (6) a boxcar smoothing filter to perform time-based boxcar smoothing. The filtered data may then be fed to classification layer 304 of decoder module 300.

[0101] like Figure 3A As shown, the computing device may include at least one decoder module 300 and a neural feedback module 308. The decoder module 300 may further include one or more preprocessing layers 302 and classification layers 304. As described below, the decoder module can select different decoder settings based on the individual BCI user's environment or application factors.

[0102] In another variation, the systems and methods described herein allow BCI systems to automatically or selectively use different decoder settings based on one or more factors. These factors may include, but are not limited to, environmental or application factors.

[0103] Some decoders are more suitable than others in certain situations. BCI decoders are often used to change the state of computer applications. Some state transitions have higher potential functional and social consequences than others (e.g., pressing send on an email compared to typing characters). Changing the decoder based on its relative accuracy / speed and the current context of the application state may be appropriate. However, changing the decoder can be inconvenient and may undermine an individual's autonomy in having to manually switch decoders.

[0104] Application software can detect various information via commercially available platforms, including mobile device platforms, including what type of application is being used, and information about where the patient is and how they are using the system. This "context" can include ongoing user activities such as typing, using social media, enabling a "tiled" graphical user interface, browsing an internet browser, interacting with a mixed reality headset, navigating maps, watching videos, playing computer games, etc. Context can also include environmental factors such as temperature, lighting, and ambient noise; external physiological data, such as heart rate, may be part of the Apple Health ecosystem or other similar systems.

[0105] Depending on the context, the system can adjust its decoding settings based on this information or various other information. Such adjustments may include changing the algorithm, altering the underlying neural networks being used by the decoder, or both.

[0106] Different algorithms have different characteristics. For example, asynchronous switching algorithms make predictions on a quasi-continuous basis (e.g., every 100ms). This contrasts with synchronous switching algorithms, which predict timescales at which the user can respond appropriately (e.g., 2-4 seconds). Therefore, asynchronous algorithms tend to produce more false positives than synchronous algorithms. Context-aware systems may want to switch to a more accurate but slower synchronous decoder when triggering high-consequence application state changes (e.g., sending email).

[0107] Figure 3B This illustrates a basic example of using context to determine which decoder 80, 82 to use to decode signals in a BCI. In this example, an individual is monitored to obtain contextual data about the individual's behavior, condition, or situation. This contextual data can include any information that will be relevant to the individual. For example, such data includes information about which electronic devices are currently connected to the BCI system (see, for example, [link to relevant documentation]). Figure 1I The data includes: which device the individual is currently using, screen / GUI characteristics (keyboard, tiled format, or other content), system status (locked or in use), whether one or more components of the BCI system and electronic devices are powered on or off, receiver and transmitter battery status, accelerometer data to determine whether the individual is moving or stationary, which terminal device the host device is using (connection information), and the user's physiological data (e.g., heart rate, fatigue, caffeine, body temperature, blood pressure, etc.).

[0108] Contextual data serves as input to determine which decoding algorithm or process (e.g., 80 or 82) to use to produce the output for the individual to use in enabling BCI systems and / or associated electronic devices. Although Figure 3B The illustration shows two decoding options, 80 and 82, but the system can include any number of decoding algorithms or processes.

[0109] In practice, the neural interface device 100 is configured to detect an individual's brain activity. Components such as receiver and transmitter units or signal control units generate electronic signals and transmit them to a computer processor (see, for example, Figure 1E and...). Figure 1IThe system includes a signal control unit 120, an external computer 130, etc. The system (typically a computer processor) also monitors the individual to generate contextual data. Alternatively or in combination, contextual data may be generated from electronic devices the individual is using, physiological data relevant to the individual, and / or specific actions the individual takes when activating the electronic devices. The electronic signals are then processed by the computer processor to produce an output signal, wherein the computer processor is configured to selectively apply at least one of a plurality of algorithms to decode the electronic signals, wherein the selection of at least one algorithm depends at least in part on the contextual data. This output signal is then electronically transmitted to one or more external electronic devices, enabling the individual to interact with one or more external electronic devices using brain activity (e.g., see...). Figure 1I (Transmissions 22, 24, 26, etc.).

[0110] Contextual data can be generated by proactively monitoring an individual before or during their interaction with one or more external electronic devices. For example... Figure 1I As shown, electronic devices can provide contextual data, which includes information related to the electronic device (e.g., the type of device, the type of control required, etc.).

[0111] In addition, different neural phenomena also have different characteristics. This can be in terms of signal-to-noise ratio, time scale of performance, and action context. For example, oscillatory bursts are consciously generated and act rapidly, thus making them suitable for applications such as typing. On the other hand, error-related potentials (ErrPs) are not consciously generated, but can indicate when an erroneous action was taken [1]. Switching the decoder after an application state change to look for ErrPs may be a useful task.

[0112] In some embodiments, such as Figures 4A-4E The neurofeedback graphical user interface (GUI) 400 shown can be displayed on a monitor 212 communicatively coupled to a computing device or processor. An individual can view the neurofeedback GUI 400 on monitor 212 while performing neurofeedback training. Movable graphical elements 406 (see, for example, as will be discussed in more detail later) Figures 4A-4E and Figures 5A-5C The brain activity recorded by the recording device 100 can be displayed on the neurofeedback GUI 400, representing the individual's current brain activity. The neurofeedback GUI 400 and graphical element 406 can be constructed in such a way that the complexity of the brain activity recorded by the recording device 100 is reduced to a form that is accessible and easy to understand for the individual. Furthermore, the neurofeedback GUI 400 and graphical element 406 can help the individual generate brain activity consistent with their desired intentions.

[0113] The computing device can be trained to map or associate previously recorded brain activity with a specific intention 408, converting brain activity recorded by the recording device 100 into predictions about an individual's intention 408. For example, the computing device can be trained using training set data collected from the individual as they repeatedly initiate, maintain, and terminate a specific intention 408. During these training sessions, the individual's brain activity can be recorded by the recording device 100.

[0114] Once the computing device has been trained or calibrated using training set data collected from individuals, it can control certain peripheral devices or software applications running on those peripheral devices based on a predicted individual intent 408. For example, the computing device may be communicatively coupled to a peripheral device (i.e., wired or wirelessly connected to it), such as a personal electronic device, an IoT device, a mobile vehicle, or a software application running on that peripheral device. The computing device can transmit signals or commands to the peripheral device or software application in response to the individual's predicted intent 408 to control the operation or function of the peripheral device or software application. For example, in response to an individual's intent 408 to make or perform a movement of the individual's left hand, the computing device may instruct a mobile vehicle (e.g., a wheelchair) transporting the individual to move in a forward direction.

[0115] However, as mentioned earlier, an individual's ability to successfully control peripheral devices or software applications using the BCI system depends on their capacity to self-regulate brain activity and consistently generate brain activity consistent with intention 408. Therefore, neurofeedback training can improve an individual's control over the BCI system 100 and ultimately improve their control over one or more peripheral devices that are communicatively coupled to the BCI system 100 or software applications running on such peripheral devices.

[0116] Classification layer 304 may include one or more machine learning algorithms or classifiers 306, with the intent 408 to classify the obtained data segments or segments into individuals (see [link to documentation]). Figures 4A-4E and Figures 6-8 In some embodiments, the machine learning algorithm or classifier 306 may be a supervised learning model, such as a support vector machine (SVM). In other embodiments, the machine learning algorithm or classifier may be a Gaussian mixture model classifier, a Naive Bayes classifier, or another type of machine learning classifier.

[0117] The classification layer 304 can be trained or calibrated to classify or predict an individual's intention 408 based on previously recorded brain activity. For example, the classification layer 304 can predict an individual's intention 408 multiple times per second. The classification layer 304 can be trained using training data collected from individuals.

[0118] In some implementations, the training phase may involve an individual repeatedly initiating, maintaining, and terminating a specific thought or attempting a specific action while their brain activity is recorded by device 100. For example, such a training session may involve an individual repeatedly resting for 5 seconds and then attempting to move their left hand for 5 seconds. The individual's brain activity during this training period may be recorded, and the recorded brain activity may be mapped to the individual's intentions 408 to rest and move their left hand, respectively.

[0119] As shown in Figure 3, the classification layer 304 can feed the individual's prediction intent 408 to the neural feedback module 308. In some embodiments, the neural feedback module 308 can be configured to construct certain neural feedback GUIs (see, for example, Figures 4A-4E and Figure 6 The neurofeedback module 308 can be displayed to an individual via a display 212 communicatively coupled to a computing device to help the individual generate or reconstruct brain activity consistent with the individual's desired intention. In other embodiments, the neurofeedback module 308 can also transmit commands or signals to a user output device (e.g., a speaker or haptic feedback component) communicatively coupled to the computing device to generate user output (e.g., auditory or haptic feedback) to help the individual generate brain activity consistent with the individual's desired intention. The neurofeedback module 308 will be discussed in more detail in the following sections.

[0120] Further details regarding the invention can be found using materials and manufacturing techniques applicable to those skilled in the art. The same applies to the method-based aspects of the invention, in terms of additional actions typically or logically employed. Furthermore, although the invention has been described with reference to several examples, and various features have been optionally incorporated, the invention is not limited to the descriptions or indications contemplated with respect to each variation thereof.

[0121] Various changes may be made to the described invention and equivalents (whether or not they are described herein or included for brevity) without departing from the true spirit and scope of the invention. Furthermore, any optional features of variations of the invention may be set forth and claimed independently or in combination with any one or more of the features described herein. Therefore, where possible, the invention contemplates combinations of various aspects of the embodiments or combinations of the embodiments themselves. References to singular items include the possibility of multiple identical items. More specifically, as used herein and in the appended claims, unless the context clearly specifies otherwise, the singular forms “a,” “and,” “said,” and “the” include plural references.

[0122] It is important to note that, where possible, aspects or embodiments of the various described embodiments may be combined. Such combinations are intended to be within the scope of this disclosure.

Claims

1. A method for decoding an electronic signal, said electronic signal being generated by a neural interface device configured to detect brain activity in an individual, said method comprising: The electronic signal is transmitted to the computer processor; Contextual data is generated by monitoring the individuals mentioned above; The electronic signal is processed using the computer processor to generate an output signal, wherein the computer processor is configured to selectively apply at least one of a plurality of algorithms to decode the electronic signal, wherein the selection of the at least one algorithm depends at least in part on the context data; and The output signal is electronically transmitted to one or more external electronic devices, enabling the individual to interact with the one or more external electronic devices using brain activity.

2. The method according to claim 1, wherein, The contextual data is generated by monitoring the individual, which occurs before or during the interaction between the individual and one or more external electronic devices.

3. The method according to claim 1, wherein, Before processing the electronic signal, the computer processor confirms that the electronic signal represents brain activity intentionally generated by the individual.

4. The method according to claim 1, wherein, The one or more external electronic devices include a plurality of additional electronic devices, and the context data includes information related to the plurality of additional electronic devices.

5. The method according to claim 4, wherein, The contextual data includes information about the electronic devices among the plurality of additional electronic devices actively activated by the individual.

6. The method according to claim 4, wherein, The contextual data includes information about which of the plurality of additional electronic devices are coupled to the computer processor.

7. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes: acquiring data about environmental factors related to the individual.

8. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes: acquiring data about health information related to the individual.

9. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes: acquiring data about how the individual uses the one or more external electronic devices.

10. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes: acquiring data about whether the individual attempts to move the cursor on one or more external electronic devices.

11. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes acquiring data about whether the individual attempts to electronically input text into one or more external electronic devices.

12. The method according to claim 1, wherein, The context data selects at least one algorithm to reduce the delay of the output signal.

13. The method according to claim 1, wherein, The context data selects at least one algorithm to increase the delay of the output signal.

14. The method according to claim 1, wherein, The contextual data selects at least one algorithm to increase the accuracy of the output signal.

15. The method according to claim 1, wherein, The context data selects at least one algorithm to increase the speed at which the output signal is generated.

16. The method according to claim 1, wherein, The context data selects at least one algorithm to generate the output signal as a continuous output signal.

17. The method according to claim 1, wherein, The context data selects at least one algorithm to generate the output signal as a discrete output signal.

18. The method according to claim 1, wherein, The computer processor is located within a signal control unit, which includes a housing structure physically separated from the neural interface device and configured to be portable, wherein electronically transmitting the output signal to one or more external electronic devices includes electronically transmitting the output signal from the signal control unit.

19. The method according to claim 18, wherein, Generating the contextual data by monitoring the individual includes obtaining data about which of the one or more external electronic devices is operatively connected to the signal control unit.

20. The method according to claim 18, wherein, Generating the contextual data by monitoring the individual includes acquiring data about multiple electronic communication modes operatively connected to the signal control unit.

21. The method according to claim 18, wherein, Generating the contextual data by monitoring the individual includes: acquiring data on the activity status of the signal control unit.

22. The method according to claim 1, wherein, Generating the contextual data by monitoring the individual includes monitoring previous output signals transmitted by the individual to the one or more external electronic devices.

23. A method for facilitating interaction between an individual and one or more electronic devices when the individual uses a neural interface device, the neural interface device being configured to generate electronic signals decoded from the individual's brain activity, the method comprising: The electronic signal is transmitted to the computer processor; Contextual input data is generated by monitoring contextual information related to the individual. The electronic signal is processed using the computer processor to selectively apply at least one of a plurality of algorithms to decode the electronic signal to generate an output signal, wherein the selection of the at least one algorithm depends at least in part on the context input data; and The output signal is electronically transmitted to one or more electronic devices, enabling the individual to interact with the one or more electronic devices using brain activity.

24. The method according to claim 23, wherein, The contextual input data is generated by monitoring contextual information related to the individual, occurring before or during the interaction between the individual and one or more electronic devices.

25. The method according to claim 23, wherein, Before processing the electronic signal, the computer processor confirms that the electronic signal represents brain activity intentionally generated by the individual.

26. The method according to claim 23, wherein, The one or more electronic devices include a plurality of additional electronic devices, and wherein the context input data includes information related to the plurality of additional electronic devices.

27. The method according to claim 26, wherein, The contextual input data includes information about the electronic devices among the plurality of additional electronic devices enabled by the individual.

28. The method according to claim 27, wherein, The context input data includes information about which of the plurality of additional electronic devices are coupled to the computer processor.

29. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes: acquiring data about environmental factors related to the individual.

30. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes: acquiring data about health information related to the individual.

31. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes obtaining data about how the individual uses the one or more electronic devices.

32. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes obtaining data on whether the individual attempts to move the cursor on one or more electronic devices.

33. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes: acquiring data on whether the individual attempted to electronically input text on one or more electronic devices.

34. The method according to claim 23, wherein, The context input data selects at least one algorithm to reduce the delay of the output signal.

35. The method according to claim 23, wherein, The context input data selects at least one algorithm to increase the delay of the output signal.

36. The method according to claim 23, wherein, The context input data selects at least one algorithm to increase the accuracy of the output signal.

37. The method according to claim 23, wherein, The context input data selects at least one algorithm to increase the speed at which the output signal is generated.

38. The method according to claim 23, wherein, The context input data selects at least one algorithm to generate the output signal as a continuous output signal.

39. The method according to claim 23, wherein, The context input data selects at least one algorithm to generate the output signal as a discrete output signal.

40. The method according to claim 23, wherein, The computer processor is located within a signal control unit, which includes a housing structure physically separated from the neural interface device and configured to be portable, and wherein electronically transmitting the output signal to one or more electronic devices includes electronically transmitting the output signal from the signal control unit.

41. The method according to claim 40, wherein, Generating the contextual input data by monitoring the individual includes obtaining data about which of the one or more electronic devices is operatively connected to the signal control unit.

42. The method according to claim 40, wherein, Generating the contextual input data by monitoring the individual includes acquiring data about multiple electronic communication modes operatively connected to the signal control unit.

43. The method according to claim 40, wherein, Generating the contextual input data by monitoring the individual includes: acquiring data on the activity status of the signal control unit.

44. The method according to claim 23, wherein, Generating the contextual input data by monitoring the individual includes monitoring previous output signals transmitted by the individual to the one or more electronic devices.

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