System and method for genetic control using neural signal

Abstract

To provide universal switch modules, universal switches, and methods of using the same.SOLUTION: Methods include a method for preparing an individual to interface with an electronic device or software. For example, a method is disclosed which can include measuring brain-related signals of the individual to obtain a first sensed brain-related signal when the individual generates a task-irrelevant thought. This method can include transmitting the first sensed brain-related signal to a processing unit. The method can also include associating the task-irrelevant thought and the first sensed brain-related signal with N input commands. Furthermore, the method can include compiling the task-irrelevant thought, the first sensed brain-related signal, and the N input commands to an electronic database.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] The present disclosure relates to methods of using neural-related signals, and more particularly, to methods of using neural signals as universal switches. [Background technology]

[0002] Today, in brain-computer interfaces (BCIs), users are asked to either perform task-related mental tasks to perform a given target task (e.g., if the target task is to move a cursor, they try to move the cursor) or task-irrelevant mental tasks to perform a given target task (e.g., they try to move their hand to move the cursor to the right). Furthermore, today's BCIs only allow users to use their thoughts (e.g., task-related mental tasks and task-irrelevant mental tasks) to control target tasks preset by researchers. This disclosure describes novel methods and systems that prepare and enable BCI users to use thoughts that are not related to a given task to independently control various end applications, including software and devices. Summary of the Invention [Problem to be solved by the invention]

[0003] Systems and methods for control using neural-related signals are disclosed, including universal switches and methods of use thereof. [Means for solving the problem]

[0004] A method for preparing a human to interface with electronic devices and software is disclosed. For example, the method may include measuring neural-related signals of the human when the human generates a first non-task-related thought to obtain a first detected neural signal. The method may include transmitting the first detected neural signal to a processing unit. The method may include associating the first non-task-related thought and the first detected neural signal with a first input command. The method may include collecting and organizing the first non-task-related thought, the first detected neural signal, and the first input command in an electronic database.

[0005] A method for controlling a first device and a second device is disclosed. For example, the method may include measuring neural-related signals of a human when the human generates non-task-related thoughts to obtain a detected neural signal. The method may include transmitting the detected neural signal to a processor. The method may include associating, by the processor, the detected neural signal with a first device input command and a second device input command. The method may include, upon associating the detected neural signal with the first device input command and the second device input command, electrically transmitting the first device input command to the first device or electrically transmitting the second device input command to the second device.

[0006] A method for preparing a human to interface with a first device and a second device is disclosed. For example, the method may include measuring brain-related signals of the human to obtain detected brain-related signals when the human generates task-specific thoughts by contemplating a first task. The method may include transmitting the detected brain-related signals to a processor. The method may include associating, by the processor, the detected brain-related signals with a first device input command associated with the first device task. The first device task is different from the first task. The method may include associating, by the processor, the detected brain-related signals with a second device input command associated with a second device task. The second device task is different from the first device task and the first task. The method may include, upon associating the detected brain-related signal with a first device input command and a second device input command, electrically transmitting the first device input command to the first device to perform a first device task associated with the first device input command, or electrically transmitting the second device input command to the second device to perform a second device task associated with the second device input command. [Brief explanation of the drawings]

[0007] [Figure 1A] FIG. 1A illustrates a universal switch module according to one embodiment. [Figure 1B] FIG. 1B illustrates a universal switch module according to one embodiment of FIG. 1A when a patient is thinking. [Figure 1C] FIG. 1C illustrates an exemplary user interface for one embodiment of a host device for the universal switch module of FIGS. 1A and 1B. [Figure 2A] 2A-2D illustrate a universal switch model according to one aspect for communicating with an end application. [Figure 2B] 2A-2D illustrate a universal switch model according to one aspect for communicating with an end application. [Figure 2C] 2A-2D illustrate a universal switch model according to one aspect for communicating with an end application. [Figure 2D] 2A-2D illustrate a universal switch model according to one aspect for communicating with an end application. [Figure 3] FIG. 3 illustrates a wireless universal switch module according to one embodiment communicating with an end application. [Figure 4] FIG. 4 illustrates a universal switch module according to one embodiment used to record nerve-related signals in a patient. [Figure 5] FIG. 5 illustrates a method performed in the universal switch module of FIGS. 1A-1C according to one embodiment. [Figure 6] FIG. 6 illustrates a method performed in the universal switch module of FIGS. 1A-1C according to one embodiment. [Figure 7] FIG. 7 illustrates a method performed in the universal switch module of FIGS. 1A-1C according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] The drawings shown and described in the drawings are of illustrative embodiments and are not limiting. Like reference numbers generally indicate identical or functionally equivalent elements.

[0009] Universal switch modules, universal switches, and methods for using them are disclosed. For example, FIGS. 1A-1C illustrate one embodiment of a universal switch module 10 that can be used by a patient 8 (e.g., a BCI user) to control one or more end applications 12 by conjuring a thought 9. The module 10 can include a neural interface 14 and a host device 16. The module 10 (e.g., the host device 16) can communicate with one or more end applications 12 via wired and / or wireless communication. The neural interface 14 can be a biological medium signal detector (e.g., an electrical conductor, a biochemical sensor), the host device 16 can be a computer (e.g., a laptop, a smartphone), and the end application 12 can be any electronic device or software. The neural interface 14 can monitor neural-related signals 17 of the biological medium via one or more sensors. The processor of module 10 can analyze the detected neural-related signal 17 to determine whether the detected neural-related signal 17 is associated with a thought 9 assigned to an input command 18 of an end application 12. Once a thought 9 assigned to an input command 18 is detected by neural interface 14 and associated with the input command 18 by the processor, the input command 18 can be transmitted (e.g., via a processor, controller, or transceiver) to the end application 12 with which the input command 18 is associated. A thought 9 can be assigned to input a command 18 of multiple end applications 12. In this way, module 10 advantageously enables patient 8 to independently control multiple end applications 12, e.g., a first end application and a second end application, with a single thought (e.g., thought 9), and the thought 9 can be used to control the first application and the second application at different times and / or simultaneously.In this way, the module 10 can function as a universal switch module, capable of controlling multiple end applications 12 (e.g., software and devices) using the same thought 9. The thought 9 can be a universal switch that can be assigned to any input command 18 of any end application 12 (e.g., to an input command 18 of a first end application and to an input command 18 of a second end application). The first end application can be a first device or first software. The second end application can be a second device or second software.

[0010] When the patient 8 thinks a thought 9, the input command 18 associated with the thought 9 can be transmitted by the module 10 (e.g., via a processor, controller, or transceiver) to the corresponding end application 12. For example, if the thought 9 is assigned to an input command 18 for a first end application, the input command 18 for the first end application can be transmitted to the first end application when the patient 8 thinks the thought 9, and if the thought 9 is assigned to an input command 18 for a second end application, the input command 18 for the second end application can be transmitted to the second end application when the patient 8 thinks the thought 9. The thought 9 can thereby interface with or control multiple end applications 12, functioning like a universal button (e.g., thought 9) on a universal controller (e.g., the patient's brain). Any number of thoughts 9 can be used as switches. The number of thoughts 9 used as switches can correspond, for example, to the number of controls (e.g., input commands 18) needed or desired to control the end applications 12.

[0011] Using a video game controller as an example, a patient's thoughts 9 can be assigned to any input command 18 associated with any individual button, any combination of buttons, and any directional movement (e.g., of a joystick, directional pad, or other control pad) on the controller, allowing the patient 8 to play any game on any video game system using thoughts 9, with or without the presence of a traditional physical controller. A video game system is just one example of an end application 12. Module 10 allows the patient's thoughts 9 to be assigned to input commands 18 for any end application 12, such that the patient's thoughts 9 can be mapped to the control of any software or device. This allows Module 10 to organize the patient's thoughts 9 into groups of assignable switches that are essentially universal but whose execution is specific once assigned to an input command 18. Additional examples of end applications 12 include mobility devices (e.g., vehicles, wheelchairs, wheelchair lifts), prosthetic limbs (e.g., prosthetic arms, prosthetic legs), phones (e.g., smartphones), smart appliances, and smart home systems.

[0012] The neural interface 14 can detect neural-related signals 17, including those associated with thoughts 9 and those not associated with thoughts 9. For example, the neural interface 14 can have one or more sensors that can detect (also referred to as acquiring, detecting, recording, or measuring) neural-related signals 17, including those generated by the biological medium of the patient 8 when the patient 8 thinks thoughts 9, as well as those generated by the biological medium of the patient 8 not associated with thoughts 9 (e.g., causing the patient to respond to stimuli not associated with thoughts 9). The sensors of the neural interface 14 can record signals from and / or stimulate the biological medium of the patient 8. The biological medium can be, for example, neural tissue, vascular tissue, blood, bone, muscle, cerebrospinal fluid, or any combination thereof. The sensors can be, for example, electrodes, which can be any electrical conductor for detecting electrical activity in the biological medium. The sensors can be, for example, biochemical sensors. Neural interface 14 can have a single type of sensor (eg, only electrodes) or multiple types of sensors (eg, one or more electrodes and one or more biochemical sensors).

[0013] The neural-related signal may be any signal (e.g., electrical, biochemical) detectable from a biological medium, any one or more elements extracted (e.g., via a computer processor) from the detected neural-related signal, or both, where the extracted elements may be or include characteristic information about the patient's 8 thoughts (9) such that different thoughts (9) can be distinguished from one another. Alternatively, the neural-related signal may be an electrical signal, any signal (e.g., a biochemical signal) caused by an electrical signal, any one or more elements extracted (e.g., via a computer processor) from the detected neural-related signal, or any combination thereof. The neural-related signal may be a neural signal such as an electroencephalogram (EEG). If the biological medium is within the patient's skull, the neural-related signal may be, for example, a brain signal (e.g., detected from brain tissue) resulting from or caused by the patient's 8 having a thought (9). Thus, the neural-related signal may be a brain-related signal, such as an electrical signal from any one or more portions of the patient's brain (e.g., motor cortex, sensory cortex). If the biological medium is outside the patient's skull, the neural-related signals may be, for example, electrical signals associated with muscle contractions (e.g., of a body part, such as an eyelid, eye, nose, ear, finger, arm, toe, or leg) resulting from or caused by the patient 8 having a thought 9. The thought 9 (e.g., a body part movement, memory, task) that the patient 8 has when the neural-related signals are sensed from brain tissue may be the same as or different from the thought 9 that the patient 8 has when the neural-related signals are sensed from non-brain tissue. Neural interface 14 may be located inside the patient's brain, outside the patient's brain, or both.

[0014] The module 10 can include one or more neural interfaces 14, e.g., neural interfaces in increments within this range (e.g., one neural interface, two neural interfaces, ten neural interfaces), each of which can have one or more sensors (e.g., electrodes) configured to detect neural-related signals (e.g., neural signals). The location of the neural interface 14 can be selected to optimize recording of the neural-related signals, e.g., by selecting a location where the signal is strongest, where interference from noise is minimized, where trauma to the patient 8 caused by implantation or engagement (e.g., via surgery) of the neural interface 14 to the patient 8 is minimized, or any combination thereof. For example, the neural interface 14 can be a brain-machine interface, such as an intravascular device (e.g., a stent) having one or more electrodes for detecting electrical activity in the brain. When multiple neural interfaces 14 are used, the neural interfaces 14 can be the same or different from one another. For example, if two neural interfaces 14 are used, both of the neural interfaces 14 can be intravascular devices with electrodes (e.g., expandable stents with electrodes), or one of the two neural interfaces 14 can be an intravascular device with electrodes and the other of the two neural interfaces 14 can be a device with sensors that is different from the intravascular device with electrodes.

[0015] 1A and 1B further illustrate that the module 10 can include a telemetry unit 22 configured to communicate with the neural interface 14 and a communication conduit 24 (e.g., wires) to facilitate communication between the neural interface 14 and the telemetry unit 22. The host device 16 can be configured for wired and / or wireless communication with the telemetry unit 22. The host device 16 can communicate with the telemetry unit 22 wired and / or wirelessly.

[0016] 1A and 1B further illustrate that the telemetry unit 22 can include an internal telemetry unit 22a and an external telemetry unit 22b. The internal telemetry unit 22a can communicate with the external telemetry unit 22b via wires or wirelessly. For example, the external telemetry unit 22b can be wirelessly connected to the internal telemetry unit 22a across the patient's skin. The internal telemetry unit 22a can communicate with the neural interface 14 via wires or wirelessly, and the neural interface 14 can be electrically connected to the internal telemetry unit 22a via a communication conduit 24. The communication conduit 24 can be, for example, a wire such as a stent lead.

[0017] The module 10 may include a processor (also referred to as a processing unit) that can analyze and decode the neural-related signals detected by the neural interface 14. The processor may be a computer processor (e.g., a microprocessor). The processor may apply a mathematical algorithm or model to detect the neural-related signals corresponding to when the patient 8 generates thoughts 9. For example, once the neural-related signals 17 are detected by the neural interface 14, the processor may apply a mathematical algorithm or model to detect, decode, and / or classify the detected neural-related signals 17. As another example, once the neural-related signals 17 are detected by the neural interface 14, the processor may apply a mathematical algorithm or model to detect, decode, and / or classify information in the detected neural-related signals 17. Once the neural-related signals 17 detected by the neural interface 14 are processed by the processor, the processor may associate the processed information (e.g., the detected, decoded, and / or classified neural-related signals 17, and / or the detected, decoded, and / or classified information of the detected neural-related signals 17) with input commands 18 of the end application 12.

[0018] The neural interface 14, the host device 16, and / or the telemetry unit 22 can include a processor. Alternatively, the neural interface 14, the host device 16, and / or the telemetry unit 22 can include a processor (e.g., a processor described above). For example, the host device 16 can analyze and decode the neural-related signals 17 sensed by the neural interface 14 via the processor. The neural interface 14 can communicate with the host device 16 wired or wirelessly, and the host device 16 can communicate with the end application 12 wired or wirelessly. Alternatively, the neural interface 14 can communicate with the telemetry unit 22 wired or wirelessly, and the telemetry unit 22 can communicate with the host device 16 wired or wirelessly, and the host device 16 can communicate with the end application 12 wired or wirelessly. Data can pass from the neural interface 14 to the telemetry unit 22, from the telemetry unit 22 to the host device 16, from the host device 16 to one or more end applications 12, or any combination thereof, e.g., to detect a thought 9 and trigger an input command 18. Alternatively, data can pass in reverse, e.g., from one or more end applications 12 to the host device 16, from the host device 16 to the telemetry unit 22, from the telemetry unit 22 to the neural interface 14, or any combination thereof, e.g., to stimulate a biological medium via one or more sensors. Data can be data collected or processed by a processor, e.g., including neural-related signals and / or features extracted therefrom. When data is flowing, e.g., from the processor to a sensor, the data can include stimulation instructions, such that, when the stimulation instructions are processed by the neural interface 14, the sensors of the neural interface can stimulate a biological medium.

[0019] 1A and 1B further illustrate that when patient 8 thinks a thought 9, the patient's 8 biological medium (e.g., the biological medium within the skull, the biological medium outside the skull, or both) can generate neural-related signals 17 that are detectable by neural interface 14. Sensors of neural interface 14 can detect the neural-related signals 17 associated with thoughts 9 when patient 8 thinks thoughts 9. The neural-related signals 17 associated with thoughts 9, features extracted from these neural-related signals 17, or both, can be assigned or associated with any input command 18 for any of end applications 12 controllable by universal switch module 10. In this way, each of the detectable neural-related signals 17 and / or their extracted features can advantageously function as a universal switch that can be assigned to any input command 18 for any end application 12. In this way, when a thought 9 is detected by the neural interface 14, an input command 18 associated with that thought 9 can be triggered and sent to the associated end application 12.

[0020] For example, when a thought 9 is detected by the neural interface 14 (e.g., via a detected neural-related signal 17), the processor may analyze (e.g., detect, decode, classify, or any combination thereof) the detected neural-related signal 17 and associate the detected neural-related signal 17 and / or features extracted therefrom with a corresponding assigned input command 18. This allows the processor to determine whether the thought 9 (e.g., the detected neural-related signal 17 and / or features extracted therefrom) is associated with one of the input commands 18. Once it is determined that the thought 9 is associated with an input command 18, the processor or controller may activate (also referred to as trigger) the input command 18. Once the input command 18 is triggered by the module 10 (e.g., by a processor or controller of the host device 16), the triggered input command 18 may be sent to its corresponding end application 12 so that the end application 12 (e.g., a wheelchair, a prosthetic hand, a smart appliance such as a coffee machine, etc.) can be controlled by the triggered input command 18. Once the end application 12 receives the triggered input command 18, the end application 12 can execute one or more instructions of the input command 18 (e.g., move the wheelchair forward at one meter per second, pinch the thumb and index finger of a prosthetic hand together, turn on a smart coffee machine, etc.). Thus, once a thought 9 (e.g., a detected neural-related signal 17 and / or features extracted therefrom) is determined to be associated with the input command 18, the input command 18 can be sent to its corresponding end application 12.

[0021] The extracted features may be components of the detected neural-related signals 17, such as a pattern of voltage fluctuations in the detected neural-related signals 17, fluctuations in power in specific frequency bands embedded in the detected neural-related signals 17, or both. For example, the neural-related signals 17 may have various ranges of vibration frequencies that correspond to when the patient 8 thinks thoughts 9. Frequencies in specific bands may contain specific information. For example, high-band frequencies (e.g., 65 Hz to 150 Hz) may contain information that correlates with movement-related thoughts, and therefore features in this high-band frequency range may be used (e.g., extracted or identified from the detected neural-related signals 17) to classify and / or decode neural events (e.g., thoughts 9).

[0022] The thought 9 can be a universal switch. The thought 9 can function (e.g., be used as) as a universal switch, and the thought 9 can be assigned to any input command 18, or vice versa. The thought 9 can be assigned or associated with any input command 18 of any of the end applications 12 controllable by the universal switch module 10 by its associated detectable neural-related signal 17 and / or features extractable therefrom. The patient 8 can activate the desired input command 18 by thinking of the thought 9 associated with the patient's desired input command 18. For example, when a thought 9 (e.g., the patient's memory of their ninth birthday party) assigned to a particular input command 18 (e.g., moving the wheelchair forward) is detected by the neural interface 14, a processor (e.g., of the host device 16) can associate the neural-related signal 17 associated with the thought 9 (e.g., the memory of the ninth birthday party) and / or features extracted therefrom with the corresponding assigned input command 18 (e.g., moving the wheelchair forward). If the detected neural-related signal (e.g., and / or the extracted features associated therewith) is associated with an assigned input command 18, the host device 16, via a processor or controller, may transmit the input command 18 to the end application 12 with which the input command 18 is associated, thereby controlling the end application 12 via the input command 18 triggered by the patient 8 having a thought 9.

[0023] If a thought 9 is assigned to multiple end applications 12 and only one of the end applications 12 is active (e.g., powered on and / or running), the host device 16 can send the triggered input command 18 to the active end application 12. As another example, if a thought 9 is assigned to multiple end applications 12 and some of the end applications 12 are active (e.g., powered on or running) and some of the end applications 12 are inactive (e.g., powered off or in standby mode), the host device 16 can send the triggered input command 18 to both the active end application 12 and the inactive end application 12. The active end application 12 can execute the input command 18 when the active end application 12 receives the input command 18. An inactive end application 12 may execute the input command 18 when the inactive application 12 becomes active (e.g., powered on or starts running), or the input command 18 may be queued (e.g., by the module 16 or the end application 12) to be executed when the inactive application 12 becomes active.As yet another example, if a thought 9 is assigned to multiple end applications 12 and two or more of the end applications 12, e.g., a first end application and a second end application, are active (e.g., powered on and / or running), the host device 16 may send a triggered input command 18 associated with the first end application to the first end application and a triggered input command 18 associated with the second end application to the second end application, or the module 10 may provide the patient 8 with a choice of which triggered input command 18 the patient 8 wants to send (e.g., send only the triggered input command 18 associated with the first end application, send only the triggered input command 18 associated with the second end application, or send both triggered input commands 18).

[0024] Thought 9 can be any thought or combination of thoughts. For example, patient 8 may have one thought, multiple thoughts, a series of multiple thoughts, multiple thoughts simultaneously, thoughts of different durations, thoughts of different frequencies, thoughts in one or more sequences, one or more combinations of thoughts, or any combination thereof. Thought 9 may be task-related, task-irrelevant, or both. Task-related thoughts are related to the patient 8's intended task, and task-irrelevant thoughts are not related to the patient 8's intended task. For example, thought 9 may be for a first task, and patient 8 may use module 10 to think about the first task in order to complete a second task (also referred to as the intended task and target task). The first task may be the same as or different from the second task. If the first task is the same as the second task, thought 9 may be a task-related thought. If the first task is different from the second task, thought 9 may be a task-irrelevant thought. For example, if the first task that the patient 8 visualizes is moving a limb (e.g., an arm, a leg), and the second task is the same as the first task, i.e., moving a prosthetic limb (e.g., an arm, a leg), then the thought 9 (e.g., the first task thought) can be a task-related thought. The prosthetic limb can be, for example, an end application 12 that the patient 8 is controlling with the thought 9. For example, if the target task is moving a cursor, then the task-related thought can be the patient 8 visualizes moving a cursor. In contrast, if the non-task-related thought 9 is that the patient 8 visualizes moving a limb (e.g., an arm) as the first task, then the second task can be a task of any end application 12 that is different from the first task, thereby making the second task a task of any end application 12 that is different from the first task of moving a limb (e.g., an arm). For example, a non-task-related thought might be that if the target task is to move a cursor to the right, patient 8 is thinking about moving a body part (e.g., hand) to the right.This allows the patient 8 to conjure a first task (e.g., a thought 9) to accomplish any second task, where the second task may be the same as or different from the first task. The second task may be any task of any end application 12. For example, the second task may be any input command 18 of any end application 12. A thought 9 (e.g., a first task) may be assigned to any second task. This allows the patient 8 to conjure a first task to trigger any input command 18 (e.g., any second task) of any end application 12. This allows the first task to advantageously function as a universal switch. Each thought 9 may generate a neural-related signal (e.g., a detectable neural-related signal 17) that is detectable and repeatable by the neural interface 14. Each detectable neural-related signal and / or a feature extractable therefrom may be a switch. The switch may be activated (also referred to as a trigger) when, for example, a sensor detects that the patient 8 has thought 9 and activated the switch, and / or when a processor determines that one or more extracted features are present in the detected neural-related signals. The switch may be a universal switch that can be assigned and reassigned to any input command 18, e.g., any set of input commands. Input commands 18 may be added, deleted, and / or modified from any set of input commands. For example, each end application 12 may have a set of input commands 18 associated with it to which neural-related signals 17 of thoughts 9 may be assigned.

[0025] Some of the thoughts 9 can be task-irrelevant thoughts (e.g., the patient 8 tries to move the cursor to the right), some of the thoughts 9 can be task-related thoughts (e.g., the patient 8 tries to move the cursor when the target task is to move the cursor), some of the thoughts 9 can be both task-irrelevant and task-related thoughts, or any combination thereof. If a thought 9 is both a task-irrelevant and a task-related thought, the thought 9 can be used as both a task-irrelevant thought (e.g., the patient 8 tries to move the cursor to the right) and a task-related thought (e.g., the patient tries to move the cursor when the target task is to move the cursor), whereby the thought 9 can be associated with multiple input commands 18, one or more of which can be task-related to the thought 9 and one or more of which can be task-irrelevant to the thought 9.

[0026] In this manner, thoughts 9 can be universal switches that can be assigned to any input command 18 in any end application 12, and each thought 9 can be assigned to one or more end applications 12. Module 10 enables each patient 8 to use their thoughts 9 like buttons on a controller (e.g., a video game controller, any control interface) to advantageously control any end application 12 desired by the patient 8. For example, a thought 9 can be assigned to each input command 18 in an end application 12, and the assigned input commands 18 can be used in any combination like buttons on a controller to control the end application 12. For example, if the end application 12 has four input commands 18 (e.g., a first input command, a second input command, a third input command, and a fourth input command, like four buttons on a controller), a different thought 9 can be assigned to each of the four input commands 18 (e.g., a first thought 9 can be assigned to the first input command 18, a second thought 9 can be assigned to the second input command 18, a third thought 9 can be assigned to the third input command 18, and a fourth thought 9 can be assigned to the fourth input command 18), thereby allowing the patient 8 to use these four thoughts 9 to activate the four input commands 18 and combinations thereof (e.g., any order, number, frequency, and duration of the four input commands 18) to control the end application 12. For example, in the case of an end application 12 having four input commands 18, the four input commands 18 may be used to control the end application 12 using any combination of the four thoughts 9 assigned to the first, second, third, and fourth input commands 18, such as a solo activation of each input command, multiple activations of each input command (e.g., two activations within five seconds, three activations within ten seconds), combinations of multiple input commands 18 (e.g., a first input command and a second input command simultaneously or in series), or any combination thereof.Each combination of thoughts 9, as well as each individual thought 9, can function as a universal switch. The patient 8 can control multiple end applications 12 with the first, second, third, and fourth thoughts 9. For example, a first thought 9 can be assigned to a first input command 18 of a first end application 12, a first thought 9 can be assigned to a first input command 18 of a second end application 12, a second thought 9 can be assigned to a second input command 18 of a first end application 12, a second thought 9 can be assigned to a second input command 18 of a second end application 12, a third thought 9 can be assigned to a third input command 18 of a first end application 12, a third thought 9 can be assigned to a third input command 18 of a second end application 12, a fourth thought 9 can be assigned to a fourth input command 18 of a first end application 12, a fourth thought 9 can be assigned to a fourth input command 18 of a second end application 12, or any combination thereof. For example, a first thought 9 can be assigned to a first input command 18 of a first end application 12 and a first input command 18 of a second end application 12, a second thought 9 can be assigned to a second input command 18 of a first end application 12 and a second input command 18 of a second end application 12, a third thought 9 can be assigned to a third input command 18 of a first end application 12 and a third input command 18 of a second end application 12, a fourth thought 9 can be assigned to a fourth input command 18 of a first end application 12 and a fourth input command 18 of a second end application 12, or any combination thereof. The first, second, third, and fourth thoughts 9 can be assigned to any application 12 (e.g., to the first and second end applications).Some thoughts may be assigned to only one application 12, and some thoughts may be assigned to multiple applications 12. Even if a thought 9 is assigned to only one application 12, a thought that is assigned to only one application 12 may be assigned to multiple applications 12 so that the patient 8 can take advantage of the universal applicability of the thought 9 (e.g., assigned to only one end application 12) as needed or desired. Alternatively, all thoughts 9 may be assigned to multiple end applications 12.

[0027] The function of each input command 18 or combination of input commands for the end application 12 can be defined by the patient 8. Alternatively, the function of each input command 18 or combination of input commands for the end application 12 can be defined by the end application 12, thereby allowing a third party to connect and assign (also referred to as mappable) the end application's input commands 18 to a set or sub-set of the patient's recurring thoughts 9. This allows third-party programs to be more accessible to suit the different desires, needs, and capabilities of each patient 8. The module 10 can advantageously be an application programming interface (API) that a third party can interface with to assign and reassign the patient's 8 thoughts 9 to various input commands 18, where each input command 18 can be activated by the patient 8 thinking of the thought 9 assigned to the input command 18 the patient 8 desires to activate, as described herein.

[0028] A patient's thoughts 9 can be assigned to input commands 18 of the end application 12 via a person (e.g., the patient or another person), a computer, or both. For example, a patient's 8 thoughts 9 (e.g., detectable neural-related signals and / or extractable features associated with the thoughts 9) can be assigned input commands 18 by the patient 8, assigned by a computer algorithm (e.g., based on signal strength of detectable neural-related signals associated with the thoughts 9), modified (e.g., reassigned) by the patient 8, modified by an algorithm (e.g., based on relative signal strength of a switch or the availability of new, repeatable thoughts 9), or any combination thereof. The input commands 18 and / or functions associated with the input commands 18 can, but need not, be unrelated to the thoughts 9 associated with activating the input commands 18. 1A-1C illustrate, in one exemplary embodiment, a non-specific or universal mode-switching program (e.g., an application programming interface (API)) to which a third party can connect and which can assign and reassign thoughts 9 (e.g., detectable neural-related signals and / or extractable features associated with thoughts 9) to various input commands 18. By assigning an input command 18 to which a thought 9 has been assigned, or vice versa, a patient 8 can use the same thought 9 for various input commands 18 in the same or different end applications 12. Similarly, by reassigning an input command 18 to which a thought 9 has been assigned, or vice versa, a patient 8 can use the same thought 9 for various input commands 18 in the same or different end applications 12.For example, a thought 9 assigned to an input command 18 that causes a prosthetic hand (e.g., a first end application) to open can be assigned to another input command 18 that causes a cursor (e.g., a second end application) to do something on a computer (e.g., any function associated with a cursor associated with a computer mouse or touchpad, including cursor movement such as left-click and right-click, and selection with the cursor).

[0029] 1A-1C further illustrate that patient 8's thoughts 9 can be assigned to multiple end applications 12, allowing patient 8 to switch between multiple end applications 12 without having to reassign input commands 18 each time patient 8 uses a different end application 12. For example, thoughts 9 can be assigned to multiple end applications 12 simultaneously (e.g., a thought 9 can be assigned to both a first end application and a second end application, and the process of assigning thoughts 9 to both the first end application and the second end application can, but need not, occur simultaneously). This allows patient's thoughts 9 to advantageously control any end application 12, including, for example, external gaming devices and various home appliances and devices (e.g., any smart device or system, including light switches, appliances, locks, thermostats, security systems, garage doors, windows, shades, etc.). The neural interface 14 may thereby detect non-task-related neural-related signals 17 (e.g., brain signals) for functions associated with input commands 18 of the end application 12, where the end application 12 may be any electronic device or software, including devices inside and / or outside the patient's body. As another example, the neural interface 14 may thereby detect task-related neural-related signals 17 (e.g., brain signals) for functions associated with input commands 18 of the end application 12, where the end application 12 may be any electronic device or software, including devices inside and / or outside the patient's body. As yet another example, the neural interface 14 may thereby detect neural-related signals 17 (e.g., brain signals) associated with task-related thoughts, task-unrelated thoughts, or both task-related and task-unrelated thoughts.

[0030] Some of the thoughts 9 can be task-unrelated thoughts (e.g., patient 8 tries to move his / her hand to move the cursor to the right), some of the thoughts 9 can be task-related thoughts (e.g., patient 8 tries to move the cursor when the target task is to move the cursor), some of the thoughts 9 can be both task-unrelated and task-related thoughts, or any combination thereof. If thoughts 9 are both task-unrelated and task-related thoughts, they can be used as both task-unrelated thoughts (e.g., patient 8 tries to move his / her hand to move the cursor to the right) and task-related thoughts (e.g., patient tries to move the cursor when the target task is to move the cursor), whereby thoughts 9 can be associated with multiple input commands 18, one or more of which can be task-related to thoughts 9 and one or more of which can be task-unrelated to thoughts 9. Alternatively, all of thoughts 9 can be task-unrelated thoughts. Task-non-related thoughts 9, and / or thoughts 9 used by the patient 8 as task-non-related thoughts (e.g., thoughts 9 assigned to non-thought-related input commands 18), allow the patient 8 (e.g., a BCI user) to utilize predetermined task-non-related thoughts (e.g., thoughts 9) to independently control various end applications 12, including software and devices.

[0031] 1A-1C illustrate, for example, that patient 8 can think about thought 9 (e.g., whether or not instructed to think about thought 9) and then rest. This thought-thinking task can generate detectable neural-related signals corresponding to the thought 9 that the patient was thinking. The thought-thinking task followed by rest can be performed only once, for example, when patient 8 thinks about thought 9 to control end application 12. Alternatively, the thought-thinking task can be repeated multiple times, for example, while patient 8 is controlling end application 12 by thinking about thought 9 or while the patient is training how to control end application 12 using thought 9. When neural-related signals (e.g., brain-related signals), such as neural signals, are recorded, features can be extracted (e.g., spectral power / time-frequency domain) and identified from the signal itself (e.g., time-domain signal). These features can contain characteristic information about thought 9 and can be used to identify thought 9, distinguish multiple thoughts 9 from one another, or both. Alternatively, these features can be used to develop or train a mathematical model or algorithm, such as by using machine learning techniques, that can predict the type of thought that generated the neural signal. This algorithm and / or model can be used to predict in real time what the patient 8 is thinking and associate this prediction with any desired input command 18. The process of the patient 8 thinking the same thought 9 can be repeated, for example, until the prediction provided by the algorithm and / or model matches the patient 8's thought 9. In this way, the patient 8 can calibrate each of the thoughts 9 used to control the end application 12 such that each thought 9 assigned to an input command 18 generates a repeatable neural-related signal that can be detected by the neural interface 14.The algorithm can provide feedback 19 to the patient 8 on whether the prediction matches the actual thought 9, and the feedback can be visual, auditory, and / or tactile, allowing the patient 8 to learn through trial and error. Machine learning methods and mathematical algorithms can be used to classify thoughts 9 based on features extracted and / or identified from the detected neural-related signals 17. For example, a training data set can be recorded in which the patient 8 repeats rest and thinking multiple times, and the processor can extract relevant features from the detected neural-related signals 17. Based on this data, the parameters and hyperparameters of the mathematical model or algorithm used to distinguish between rest and thinking can be optimized to predict real-time signals. The same mathematical model or algorithm tuned to predict real-time signals can then advantageously allow module 10 to convert thoughts 9 into real-time universal switches.

[0032] FIG. 1A further illustrates that the neural interface 14 can monitor a biological medium (e.g., the brain), such as an electrical signal from the tissue being monitored (e.g., neural tissue). FIG. 1A further illustrates that the neural-related signal 17 can be a brain-related signal. The brain-related signal can be, for example, an electrical signal from any one or more portions of the patient's brain (e.g., the motor cortex, the sensory cortex, etc.). As another example, the brain-related signal can be any signal (e.g., electrical, biochemical) detectable within the skull, any one or more features extracted (e.g., via a computer processor) from the detected brain-related signal, or both. As a further example, the brain-related signal can be an electrical signal, any signal (e.g., a biochemical signal) resulting from an electrical signal, any one or more features extracted (e.g., via a computer processor) from the detected brain-related signal, or any combination thereof.

[0033] FIG. 1A further illustrates that the end application 12 is separate from the module 10 but can communicate with it wired or wirelessly. As another example, the module 10 (e.g., the host device 16) can be permanently or removably attached or attachable to the end application 12. For example, the host device 16 can be removably docked with the application 12 (e.g., a device having software with which the module 10 can communicate). The host device 16 can have a port that can engage with the application 12, or vice versa. The port can be a charging port, a data port, or both. For example, if the host device is a smartphone, the port can be a Lightning port. As a further example, the host device 16 can be tethered to the application 12, for example, via a cable. The cable can be a power cable, a data transfer cable, or both.

[0034] FIG. 1B further illustrates that when patient 8 thinks of thought 9, neural-related signals 17 can be brain-related signals corresponding to thought 9. FIG. 1B further illustrates that the host device 16 can have a processor (e.g., a microprocessor) that analyzes (e.g., detects, decodes, classifies, or any combination thereof) the neural-related signals 17 received from the neural interface 14, associates the neural-related signals 17 received from the neural interface 14 with their corresponding input commands 18, associates features extracted (e.g., spectral power / time-frequency domain) or identified features (e.g., time-domain signals) from the neural-related signals 17 themselves received from the neural interface 14 with their corresponding input commands 18, stores the neural-related signals 17 received from the neural interface 14, stores the signal analysis (e.g., features extracted from or identified in the neural-related signals 17), stores the association of the neural-related signals 17 with the input commands 18, stores the association of the features extracted from or identified in the neural-related signals 17 with the input commands 18, or any combination thereof.

[0035] FIG. 1B further illustrates that the host device 16 can have memory. Data stored by the processor can be stored locally, on a server (e.g., the cloud), or both. The thoughts 9 and the resulting data (e.g., sensed neural-related signals 17, extracted features, or both) can serve as a reference library. For example, once the thoughts 9 are calibrated, the neural-related signals 17 associated with the calibrated thoughts and / or their signature (also called extracted) features can be stored. The thoughts 9 can be considered calibrated, for example, if the neural-related signals 17 and / or features extracted therefrom have a repeatable signature or characteristic that is identifiable by the processor when the neural-related signals 17 are sensed by the neural interface 14. The neural-related signals being monitored and sensed in real time can then be compared in real time to this stored calibration data. Whenever the detected signal 17 and / or one of its extracted features matches a calibrated signal, a corresponding input command 18 associated with the calibrated signal can be sent to a corresponding end application 12. For example, Figures 1A and 1B show that a patient 8 can be trained to use the module 10 by calibrating neural-related signals 17 associated with their thoughts 9 and storing those calibrations in a reference library. The training can provide feedback 19 to the patient 8.

[0036] FIG. 1C further illustrates an exemplary user interface 20 of the host device 16. The user interface 20 can be a computer screen (e.g., touchscreen, non-touchscreen). FIG. 1C illustrates an exemplary display of the user interface 20, including selectable systems 13, selectable input commands 18, and selectable end applications 12. A system 13 can be a grouping of one or more end applications 12. A system 13 can be added to or removed from the host device 16. An end application 12 can be added to or removed from the host device 16. An end application 12 can be added to or removed from the system 13. Each system 13 can have a corresponding set of input commands 18 that can be assigned to the corresponding set of end applications 12. As another example, the user interface 20 can present input commands 18 for each activated end application 12 (e.g., remote). As yet another example, the user interface 20 can present input commands 18 for activated end applications (e.g., remote) and / or deactivated end applications 12 (e.g., stimulation sleeve, phone, smart home device, wheelchair). This advantageously allows the module 10 to control any end application 12. The user interface 20 allows for easy assignment of thoughts 9 to various input commands 18 for multiple end applications 12. System grouping of end applications (e.g., System 1 and System 2) advantageously allows the patient 8 to consolidate and organize end applications 12 using the user interface 20. Pre-made systems 13 can be uploaded to the module and / or the patient 8 can create their own systems 13.For example, a first system may include all end applications 12 used by the patient 8 that are associated with mobility (e.g., wheelchair, wheelchair lift). As another example, a second system may include all end applications 12 used by the patient 8 that are associated with prosthetic limbs. As yet another example, a third system may include all end applications 12 used by the patient 8 that are associated with smart appliances. As yet another example, a fourth system may include all end applications 12 used by the patient 8 that are associated with software or devices the patient 8 uses for professional purposes. An end application 12 may reside on one or more systems 13. For example, an end application 12 (e.g., wheelchair) may reside on both System 1 and / or System 2. This organizational efficiency allows the patient 8 to easily manage end applications 12. A module 10 can have one or more systems 13, for example, from 1 to 1000 or more systems 13, including increments of 1 within this range (e.g., 1 system, 2 systems, 10 systems, 100 systems, 500 systems, 1000 systems, 1005 systems, 2000 systems). For example, FIG. 1C illustrates that a module 10 can have a first system 13a (e.g., System 1) and a second system 13b (e.g., System 2). Also, while FIG. 1C illustrates that end applications 12 can be grouped into various systems 13, each having one or more end applications 12, the user interface 20 may alternatively not group end applications into systems 13.

[0037] FIG. 1C further illustrates that a thought 9 can be assigned to an input command 18 using the host device 16. For example, a thought 9, a neural-related signal 17 associated with the thought 9, extracted features of the neural-related signal 17 associated with the thought 9, or any combination thereof can be assigned to the input command 18 of the system 13. For example, this can be done by selecting the input command 18 (e.g., left arrow) and choosing from a drop-down menu indicating the thought 9 and / or associated data (e.g., the neural-related signal 17 associated with the thought 9, extracted features of the neural-related signal 17 associated with the thought 9, or both) that can be assigned to the selected input command 18. FIG. 1C further illustrates that feedback (e.g., visual, auditory, and / or tactile feedback) can be provided to the patient 8 when the input command 18 is triggered by the thought 9 or the associated data. FIG. 1C further illustrates that one or more end applications 12 can be activated and deactivated within the system 13. An activated end application 12 may be in a powered-on state, a powered-off state, or a standby state. An activated end application 12 can receive triggered input commands 18. A deactivated end application 12 can be in a power-on state, a power-off state, or a standby state. In one example, a deactivated end application 12 is not controllable by the patient's 8 thoughts 9 unless the end application 12 is activated. Activating the end application 12 using the user interface 20 can power on the end application 12. Deactivating the end application 12 using the user interface 20 can power off the deactivated end application 12 or disconnect the module 10 from the deactivated end application 12, preventing the processor from associating the neural-related signal 17 with the thought 9 assigned to the deactivated end application 12.For example, FIG. 1C illustrates an exemplary system 1 having five end applications 12, including five devices (e.g., a remote, a stimulation sleeve, a phone, a smart home device, and a wheelchair), one of which (e.g., the remote) is activated and the others are deactivated. Once "Start" is selected (e.g., via icon 20a), patient 8 can control the end application 12 of the activated system (e.g., system 1) (e.g., remotely) using input commands 18 associated with the end application 12 of system 1. FIG. 1C further illustrates that any changes made using user interface 20 can be saved using save icon 20b, and any changes made using user interface 20 can be canceled using cancel icon 20c. FIG. 1C further illustrates that end applications 12 can be electronic devices.

[0038] 1A-1C illustrate that the same particular set of thoughts 9 can be used to control multiple end applications 12 (e.g., multiple end devices), thereby making module 10 a universal switch module. Module 10 advantageously enables patient 8 (e.g., a BCI user) to utilize thoughts (e.g., thoughts 9) unrelated to a given task to independently control various end applications 12, including, for example, multiple software and devices. Module 10 can acquire neural-related signals (e.g., via neural interface 14), decode the acquired neural-related signals (e.g., via a processor), associate (e.g., by the processor) the acquired neural-related signals 17 and / or features extracted from these signals with corresponding input commands 18 for one or more end applications 12, and control (e.g., by module 10) the multiple end applications 12. Using module 10, thoughts 9 can be advantageously used to control multiple end applications 12. For example, module 10 can be used to control multiple end applications 12, where one end application 12 can be controlled at a time. Alternatively, module 10 can be used to control multiple end applications simultaneously. Thoughts 9 can be assigned to input commands 18 for multiple end applications 12. In this manner, thoughts 9 can function as universal digital switches, and module 10 can effectively reorganize the patient's motor cortex to represent the digital switches, where each thought 9 can be a digital switch.These digital switches may be universal switches that can be used by the patient 8 to control multiple end applications 12, as each switch is assignable (e.g., via module 10) to any input command 18 of multiple end applications 12 (e.g., an input command for a first end application and an input command for a second end application). Module 10, via the processor, can distinguish between different thoughts 9 (e.g., between different switches).

[0039] Module 10 can interface with, for example, 1 to 1000 or more end applications 12, including increments of end applications 12 within this range (e.g., 1 end application, 2 end applications, 10 end applications, 100 end applications, 500 end applications, 1000 end applications, 1005 end applications, 2000 end applications). For example, FIG. 1C shows that a first system 13a can have a first end application 12a (e.g., a remote), a second end application 12b (e.g., a stimulation sleeve), a third end application 12c (e.g., a phone), a fourth end application 12d (e.g., a smart home device), and a fifth end application 12e (e.g., a wheelchair).

[0040] Each end application may have, for example, 1 to 1000 or more input commands 18 that can be associated with the patient's 8 thoughts 9, or alternatively, 1 to 500 or more input commands 18 that can be associated with the patient's 8 thoughts 9, or further alternatively, 1 to 100 or more input commands 18 that can be associated with the patient's 8 thoughts 9, including input commands 18 in increments within these ranges (e.g., 1 input command, 2 input commands, 10 input commands, 100 input commands, 500 input commands, 1000 input commands, 1005 input commands, 2000 input commands), and any sub-ranges within these ranges (e.g., 1 to 25 or less input commands 18, 1 to 100 or less input commands 18, 25 to 1000 or less input commands 18), thereby allowing any number of input commands 18 to be triggered by the patient's thoughts 9, which may be, for example, the number of input commands 18 to which the patient's 8 thoughts 9 are assigned. For example, FIG. 1C illustrates an exemplary set of input commands 18 associated with one or more activated end applications 12 (e.g., first end application 12a), including a first end application first input command 18a (e.g., left arrow), a first end application second input command 18b (e.g., right arrow), and a first end application third input command 18c (e.g., input). As another example, FIG. 1C illustrates an exemplary set of input commands 18 associated with one or more deactivated end applications 12 (e.g., second end application 12b), including a second end application first input command 18d (e.g., select output), where the second end application first input command 18d has not yet been selected but may be any input command 18 of the second end application 12b. The first end application first input command 18a is also referred to as the first input command 18a of the first end application 12a. The first end application second input command 18b is also referred to as the second input command 18b of the first end application 12a.The first end application third input command 18c is also referred to as the third input command 18c of the first end application 12a, and the second end application first input command 18d is also referred to as the first input command 18d of the second end application 12b.

[0041] When the patient 8 thinks a thought 9, the module 10 (e.g., via a processor) can associate the neural-related signals 17 associated with the thought 9 and / or features extracted therefrom with input commands 18 to which the thought 9 is assigned, and the input commands 18 associated with the thought 9 can be transmitted by the module 10 (e.g., via a processor, a controller, or a transceiver) to their corresponding end applications 12. For example, if the thought 9 is assigned to a first input command 18a in a first end application 18a, the first input command 18a in the first end application 12a can be transmitted to the first end application 12a when the patient 8 thinks the thought 9, and if the thought 9 is assigned to a first input command 18d in a second end application 12b, the first input command 18d in the second end application 12b can be transmitted to the second end application 12b when the patient 8 thinks the thought 9. This allows one thought (e.g., thought 9) to interface with or control multiple end applications 12 (first and second end applications 12a, 12b). Any number of thoughts 9 can be used as switches. The number of thoughts 9 used as switches can correspond, for example, to the number of controls (e.g., input commands 18) needed or desired to control the end applications 12. A thought 9 can be assigned to multiple end applications 12. For example, a neural-related signal 17 associated with a first thought and / or features extracted therefrom can be assigned to a first end application first input command 18a and a second end application first input command 18d.As another example, the neural-related signal 17 associated with the second thought and / or the features extracted therefrom can be assigned to a second input command 18a of a first end application and a first input command of a third end application. The first thought and the second thought may be different. Multiple end applications 12 (e.g., first and second end applications 12a, 12b) can be activated independently of each other. When the module 10 is used to control a single end application (e.g., the first end application 12a), the first thought can be assigned to multiple input commands 18. For example, the first thought alone can activate a first input command, and the first thought and the second thought together can activate a second input command that is different from the first input command. This allows the thought 9 to function as a universal switch even when only one end application 12 is controlled by the module 10, because one thought can be combined with another thought to form additional switches. Alternatively, one thought may be combined with another to form an additional universal switch that multiple end applications 12 may assign to any input command 18 controllable by module 10 via thought 9.

[0042] 2A-2D show that the neural interface 14 can be a stent 101. The stent 101 can have struts 108 and sensors 131 (e.g., electrodes). The stent 101 is both collapsible and expandable.

[0043] Figures 2A-2D further illustrate that the stent 101 can be implanted in a blood vessel in a human brain, such as a blood vessel traversing a human's superior sagittal sinus. Figure 2A illustrates an exemplary module 10, and Figures 2B-2D illustrate three enlarged views of the module 10 of Figure 2A. The stent 101 can be placed, for example, via the jugular vein into the superior sagittal sinus (SSS), which overlies the primary motor cortex, to passively record brain signals and / or stimulate tissue. The stent 101 can detect neural signals 17 associated with thoughts 9 via sensors 131, enabling, for example, individuals paralyzed due to neurological injury or disease to communicate, improve mobility, and potentially achieve independence by directly controlling their brains with assistive technology, such as end application 12. Figure 2C illustrates that a communication conduit 24 (e.g., a stent lead) can extend from the stent 101, pass through the wall of the jugular vein, and tunnel under the skin to the subclavian pouch. In this manner, communication line 24 facilitates communication between stent 101 and telemetry unit 22 .

[0044] 2A-2D further illustrate that the end application 12 can be a wheelchair.

[0045] FIG. 3 illustrates that the neural interface 14 (e.g., stent 101) can be a wireless sensor system 30 capable of wireless communication with the host device 16 (e.g., without the telemetry unit 22). FIG. 3 illustrates an embodiment in which a stent 101 in a blood vessel 104 above the motor cortex of a patient 8 picks up neural-related signals and relays this information to a wireless transmitter 32 located on the stent 101. The neural-related signals recorded by the stent 101 can be wirelessly transmitted through the patient's skull to a wireless transceiver 34 (e.g., located on the head), which then decodes and transmits the acquired neural-related signals to the host device 16. Alternatively, the wireless transceiver 34 can be part of the host device 16.

[0046] Figure 3 shows that the end application 12 can be a prosthetic hand.

[0047] 4 illustrates that a neural interface 14 (e.g., stent 101) can be used to record neural-related signals 17 from the brain, for example, from neurons in the superior sagittal sinus (SSS) or branch cortical veins. This includes (a) implanting the neural interface 14 into a blood vessel 104 in the brain (e.g., superior sagittal sinus, branch cortical vein), (b) recording the neural-related signals, (c) generating data representing the recorded neural-related signals, and (d) transmitting the data to a host device 16 (e.g., with or without a telemetry unit 22).

[0048] The entirety of U.S. patent application Ser. No. 16 / 054,657, filed August 3, 2018, is hereby incorporated by reference in its entirety for all purposes, including all systems, devices, and methods disclosed therein, including any combination of features and operations disclosed therein. For example, neural interface 14 (e.g., stent 101) can be, for example, any of the stents (e.g., stent 101) disclosed in U.S. patent application Ser. No. 16 / 054,657, filed August 3, 2018.

[0049] Using module 10, patient 8 can be prepared to interface with multiple end applications 12. Using module 10, patient 8 can perform multiple tasks using a single electronic command that is a function of a non-task-related thought (e.g., thought 9). For example, using module 10, patient 8 can perform multiple tasks with a single non-task-related thought (e.g., thought 9).

[0050] For example, FIG. 5 illustrates, in one embodiment, a method 50 of preparing a human to interface with an electronic device or software (e.g., with end application 12) having operations 52, 54, 56, and 58. FIG. 5 shows that method 50 may include, at operation 52, measuring neural-related signals of the human when the human generates a first non-task-related thought to obtain a first detected neural signal. Method 50 may include, at operation 54, transmitting the first detected neural signal to a processing unit. Method 50 may include, at operation 56, associating the first non-task-related thought and the first detected neural signal with a first input command. Method 50 may include, at operation 58, collecting and organizing the first non-task-related thought, the first detected neural signal, and the first input command in an electronic database.

[0051] As another example, FIG. 6 illustrates, in one embodiment, a method 60 for controlling a first device and a second device (e.g., first and second end applications 12a, 12b) having operations 62, 64, 66, and 68. FIG. 6 illustrates that method 60 may include, at operation 62, measuring neural-related signals of a human when the human generates non-task-related thoughts to obtain detected neural signals. Method 60 may include, at operation 64, transmitting the detected neural signals to a processor. The method may include, at operation 66, associating the detected neural signals with a first device input command and a second device input command via the processor. The method may include, at operation 68, electrically transmitting the first device input command to the first device or electrically transmitting the second device input command to the second device upon associating the detected neural signals with the first device input command and the second device input command.

[0052] As another example, FIG. 7 illustrates, in one embodiment, a method 70 for preparing a human to interface with a first device and a second device (e.g., first and second end applications 12a, 12b) having operations 72, 74, 76, 78, and 80. FIG. 7 illustrates that method 70 may include, at operation 72, measuring brain-related signals of the human when the human generates task-specific thoughts by mentally thinking about a first task to obtain detected brain-related signals. The method may include, at operation 74, transmitting the detected brain-related signals to a processor. The method may include, at operation 76, associating, via the processor, the detected brain-related signals with a first device input command associated with the first device task. The first device task may be different from the first task. The method may include, at operation 78, associating, via the processor, the detected brain-related signals with a second device input command associated with a second device task. The second device task may be different from the first device task and the first task. In operation 80, the method may include, upon associating the detected brain-related signal with the first device input command and the second device input command, electrically transmitting the first device input command to the first device to perform the first device task associated with the first device input command or electrically transmitting the second device input command to the second device to perform the second device task associated with the second device input command.

[0053] As another example, FIGS. 5-7 show variations of how a universal switch (e.g., device 9) can control multiple end applications 12.

[0054] As another example, the acts depicted in Figures 5-7 can be performed and repeated in any order and in any combination. Figures 5-7 do not limit the disclosure to the methods depicted or to the particular order of acts described. For example, the acts described in methods 50, 60, and 70 can be performed in any order, and one or more acts can be omitted or added.

[0055] As another example, in one embodiment, a method using module 10 may include measuring brain-related signals of a human when the human generates a non-task-related thought (e.g., thought 9) to obtain a first detected brain-related signal. The method may include transmitting the first detected brain-related signal to a processor. The method may include the processor applying a mathematical algorithm or mathematical model to detect a corresponding brain-related signal when the human generates thought 9. The method may include associating the non-task-related thought and the first detected brain-related signal with one or more of N input commands 18. The method may include collecting and organizing the non-task-related thought (e.g., thought 9), the first detected brain-related signal, and the N input commands 18 in an electronic database. The method may include monitoring the human for the first detected brain-related signal (e.g., using a neural interface) and, upon detecting the first detected brain-related signal, electronically transmitting at least one of the N input commands 18 to a control system. The control system may be a control system of end application 12. The N input commands 18 may be, for example, 1 to 100 input commands 18, including every increment of the input commands 18 within this range. The N input commands may be assignable to Y end applications 12, and the Y end applications may be, for example, 1 to 100 end applications 12, including every increment of the end applications 12 within this range. As another example, the Y end applications 12 may be, for example, 2 to 100 end applications 12, including every increment of the end applications 12 within this range. The Y end applications 12 may include, for example, at least one of mouse cursor control, wheelchair control, and speller control.The N input commands 18 can be at least one of a binary input associated with a task-unrelated thought, a graded input associated with a task-unrelated thought, and a continuous trajectory input associated with a task-unrelated thought. The method can include associating M sensed values ​​of the first sensed brain-related signal with the N input commands 18, where M is a sensed value between 1 and 10, inclusive. For example, if M is a sensed value of 1, the task-unrelated thought (e.g., thought 9) and the first sensed brain-related signal can be associated with the first input command (e.g., first input command 18a). As another example, if M is a sensed value of 2, the task-unrelated thought (e.g., thought 9) and the first sensed brain-related signal can be associated with the second input command (e.g., first input command 18b). As yet another example, if M is a sensed value of 3, the task-unrelated thought (e.g., thought 9) and the first sensed brain-related signal can be associated with the third input command (e.g., third input command 18c). The first, second, and third input commands can be associated with one or more end applications 12. For example, the first input command can be an input command for a first end application, the second input command can be an input command for a second end application, and the third input command can be an input command for a third application, thereby allowing one thought 9 to control multiple end applications 12. Each of the M sensed values ​​of thought 9 can be assigned to multiple end applications, thereby allowing the last number of the M sensed values ​​(e.g., 1, 2, or 3 sensed values) to function as a universal switch that can be assigned to any input command 18. The first, second, and third input commands can each be associated with a different function.The first, second, and third input commands can be associated with the same function, such that the first input command is associated with a function first parameter, the second input command is associated with a function second parameter, and the third input command is associated with a function third parameter. The first, second, and third parameters can, for example, be incremental changes in speed, volume, or both. The incremental speed can, for example, be associated with the movement of a wheelchair, the movement of a mouse cursor on a screen, or both. The incremental volume levels can, for example, be associated with the volume of an automobile sound system, the volume of a computer, the volume of a telephone, or a combination thereof. At least one of the N input commands 18 can be a click and hold command associated with a computer mouse. The method can also include associating a combination of non-task-related thoughts (e.g., thoughts 9) with the N input commands 18. The method can include associating a combination of Z task-unrelated thoughts with N input commands 18, where the Z task-unrelated thoughts can be 2 to 10 or more task-related thoughts, or more broadly, 1 to 1000 or more task-related thoughts, including unit increments therein. At least one of the Z task-unrelated thoughts can be a task-unrelated thought, and the task-unrelated thought can be a first task-unrelated thought, whereby the method can include measuring brain-related signals of the human when the human generates a second task-unrelated thought to obtain a second detected brain-related signal, transmitting the second detected brain-related signal to a processing unit, and associating the second task-unrelated thought and the second detected brain-related signal with N2 input commands, whereby if the combination of the first and second detected brain-related signals is obtained sequentially or simultaneously, the combination can be associated with N3 input commands. The task-unrelated thought can be a thought of moving a body limb. The first sensed brain-related signal may be electrical activity of brain tissue and / or functional activity of brain tissue.Any of the operations in this exemplary method may be performed in any combination and in any order.

[0056] As another example, a method of using module 10, according to one embodiment, can include measuring a human's brain-related signals to obtain a first detected brain-related signal when the human generates a first task-specific thought by contemplating a first task (e.g., by contemplating thought 9). The method can include transmitting the first detected brain-related signal to a processor. The method can include the processor applying a mathematical algorithm or mathematical model to detect a corresponding brain-related signal when the human generates the thought. The method can include associating the first detected brain-related signal with a first task-specific input command (e.g., input command 18) associated with a second task, where the second task is different from the first task (e.g., whereby thought 9 comprises a different task than the task that input command 18 is configured to perform). The first task-specific thought can be unrelated to the associating step. The method can include assigning the second task to the first task-specific command instruction, unrelated to the first task. The method can include reassigning a third task to a first task-specific command instruction, unrelated to the first task and the second task. The method can include collecting and organizing the first task-specific thought, the first detected brain-related signal, and the first task-specific input command in an electronic database. The method can include monitoring the human for the first detected brain-related signal and, upon detecting the first detected brain-related signal, electronically transmitting the first task-specific input command to a control system. The first task-specific thought can be, for example, related to a physical task, a non-physical task, or both. The generated thought can be, for example, a single thought or a complex thought. A complex thought can be two or more non-simultaneous thoughts, two or more simultaneous thoughts, and / or a sequence of two or more simultaneous thoughts. Any of the operations in this exemplary method can be performed in any combination and in any order.

[0057] As another example, in one embodiment, a method using module 10 can include measuring brain-related signals of a human when the human has a first thought to obtain a first detected brain-related signal. The method can include transmitting the first detected brain-related signal to a processing unit. The method can include the processing unit applying a mathematical algorithm or a mathematical model to detect a corresponding brain-related signal when the human generates a thought. The method can include generating a first command signal based on the first detected brain-related signal. The method can include assigning a first task to the first command signal independent of the first thought. The method can include decoupling the first thought from the first detected electrical brain activity. The method can include reassigning a second task to the first command signal independent of the first thought and the first task. The method can include collecting and organizing the first thought, the first detected brain-related signal, and the first command signal in an electronic database. The method can include monitoring the human for a first detected brain-related signal and, upon detecting the first detected brain-related signal, electronically transmitting a first input command to a control system. The first thought can include, for example, a real or imagined muscle contraction, a real or imagined memory, or both, or any abstract thought. The first thought can be, for example, a single thought or a complex thought. Any of the operations in this exemplary method can be performed in any combination and in any order.

[0058] As another example, in one embodiment, a method using module 10 can include measuring electrical activity of a human's brain tissue when the human has a first thought to obtain first detected electrical brain activity. The method can include transmitting the first detected electrical brain activity to a processing unit. The method can include the processing unit applying a mathematical algorithm or mathematical model to detect brain-related signals corresponding to when the human generates a thought. The method can include generating a first command signal based on the first detected electrical brain activity. The method can include assigning a first task and a second task to the first command signal. The first task can be associated with a first device, and the second task can be associated with a second device. The first task can be associated with a first application on the first device, and the second task can be associated with a second application on the first device. The method can include assigning the first task to the first command signal independent of the first thought. The method can include assigning the second task to the first command signal independent of the first thought. The method can include collecting and organizing the first thought, the first sensed electrical brain activity, and the first command signal in an electronic database. The method can include monitoring the human for the first sensed electrical brain activity and, upon detecting the first sensed electrical brain activity, electronically transmitting the first command signal to a control system. Any of the operations in this exemplary method can be performed in any combination and in any order.

[0059] As another example, in one embodiment, a method using module 10 can include measuring neural-related signals of a human to obtain a first detected neural signal when the human generates a non-task-related thought. The method can include transmitting the first detected neural signal to a processor. The method can include the processor applying a mathematical algorithm or mathematical model to detect a brain-related signal corresponding to the human generating a non-task-related thought. The method can include associating the non-task-related thought and the first detected neural signal with a first input command. The method can include collecting and organizing the non-task-related thought, the first detected neural signal, and the first input command in an electronic database. The method can include monitoring the human for the first detected neural signal and, upon detecting the first detected neural signal, electronically transmitting the first input command to a control system. The neural-related signal can be a brain-related signal. The neural-related signal can be measured from neural tissue in the human's brain. Any of the operations in this exemplary method can be performed in any combination and in any order.

[0060] As another example, in one embodiment, a method using module 10 can include measuring a neural-related signal of a human to obtain a first detected neural-related signal when the human generates a first task-specific thought by thinking about a first task. The method can include transmitting the first detected neural-related signal to a processor. The method can include the processor applying a mathematical algorithm or mathematical model to detect a corresponding brain-related signal when the human generates a thought. The method can include associating the first detected neural-related signal with a first task-specific input command associated with a second task, where the second task is different from the first task, thereby providing a user with a mechanism for controlling multiple tasks having different task-specific inputs with a single user-generated thought. The method can also include collecting and organizing the task-unrelated thoughts, the first detected neural signal, the first input command, and the corresponding tasks in an electronic database. The method may include utilizing a memory of an electronic database to automatically group and automatically map control functions for automatic setup of the system for use with combinations of non-task-related thoughts, detected brain-related signals, and one or more N inputs based on tasks, brain-related signals, or thoughts. The neural-related signals may be neural-related signals of brain tissue. Any of the operations in this exemplary method may be performed in any combination and in any order.

[0061] Module 10 may perform any combination of any of the methods and may perform any of the actions of any of the methods disclosed herein.

[0062] The claims are not limited to the exemplary embodiments shown in the drawings, but may instead claim any element disclosed or contemplated throughout this disclosure. Elements described herein as singular can be made plural (i.e., "one" can be made plural). Species of a person may have characteristics or elements of other species of that person. Some elements may be omitted for clarity of illustration. The configurations, elements, or complete assemblies, and methods and elements thereof for implementing the present disclosure described above, as well as variations of aspects of the present disclosure, can be combined and modified with each other in any combination, and each combination is expressly disclosed herein. All devices, apparatuses, systems, and methods described herein can be used for medical (e.g., diagnostic, therapeutic, rehabilitative, etc.) or non-medical purposes. "May" and "can" are interchangeable (e.g., "may" can be replaced with "can" and "can" with "may"). Any disclosed range can include any subrange of the disclosed range, for example, a range of 1 to 10 units can include 2 to 10 units, 8 to 10 units, or any other subrange. Any phrase containing the construction "A and / or B" means (1) A alone, (2) B alone, (3) both A and B, or any combination of (1), (2), and (3), for example, (1) and (2), (1) and (3), (2) and (3), and (1), (2), and (3).For example, in this disclosure, the sentence "a module 10 (e.g., a host device 16) can communicate with one or more end applications 12 wired and / or wirelessly" may include (1) a module 10 (e.g., a host device 16) can communicate with one or more end applications 12 wiredly, (2) a module 10 (e.g., a host device 16) can communicate with one or more end applications 12 wirelessly, (3) a module 10 (e.g., a host device 16) can communicate with one or more end applications 12 wired and wirelessly, or any combination of (1), (2), and (3).

Claims

1. a nerve-related signal measuring step of measuring a nerve-related signal of the human when the human generates a thought associated with a muscle contraction and not related to the task to obtain a detected nerve signal; transmitting the sensed neural signals to a processing unit; associating the task-unrelated thought and the detected neural signal with a universal switch in the processing unit, the universal switch being assigned to a first input command of an electronic device in the processing unit; and collecting the non-task-related thoughts, the detected neural signals, and the first input command into an electronic database, whereby the human controls the electronic device by producing the non-task-related thoughts and electrically communicating the first input command to the electronic device.

2. The method of claim 1 , wherein the first input command is an input command of a first end application.

3. The method of claim 2 , further comprising associating the non-task-related thoughts and the sensed neural signals with a second input command.

4. The method of claim 3 , wherein the second input command is an input command of a second end application.

5. 5. The method of claim 4, further comprising the step of controlling the first end application with the first input command upon detecting the sensed neural signal.

6. 6. The method of claim 5, further comprising the step of controlling the second end application with the second input command upon detecting the sensed neural signal.

7. 5. The method of claim 4, further comprising collecting the non-task-related thoughts, the sensed neural signals, and the second input command in the electronic database.

8. The method of claim 4 , wherein the sensed neural signals are sensed electrical brain activity.

9. The method of claim 4 , wherein measuring the human's nerve-related signals comprises measuring the human's nerve-related signals with an implanted intravascular device.

10. monitoring the human for the detected neural signals; upon detecting the sensed neural signal, electrically transmitting the first input command to the first end application or the second end application; The method of claim 4 further comprising:

11. 11. The method of claim 10, further comprising: monitoring the human for the sensed neural signal; and, upon detecting the sensed neural signal, electronically transmitting the first input command to the first end application and the second end application.

12. a nerve-related signal measuring step of measuring a nerve-related signal of the human when the human generates a thought associated with a muscle contraction and not related to the task, to obtain a detected nerve signal; transmitting the detected neural signals to a processor; calibrating, via the processor, a universal switch that is assigned to an input command of a first device and that is assigned to an input command of a second device based on the detected neural signals; collecting the task-unrelated thoughts, the detected neural signals, the input commands of the first device, and the input commands of the second device in an electronic database, whereby the human can control the first device or the second device by generating task-unrelated thoughts to cause the input commands of the first device to be electrically transmitted to the first device or the input commands of the second device to be electrically transmitted to the second device; 10. A method of calibrating a neural signal as an electronic switch to enable a human to individually and selectively control a first device and a second device, comprising:

13. 13. The method of claim 12, wherein collecting the task-unrelated thoughts, the detected neural signals, the input commands of the first device, and the input commands of the second device in the electronic database enables the human to control the first device and the second device by generating the task-unrelated thoughts to cause the input commands of the first device to be electronically transmitted to the first device and the input commands of the second device to be electronically transmitted to the second device.

14. 13. The method of claim 12, wherein the sensed neural signals are sensed electrical brain activity.

15. 13. The method of claim 12, wherein measuring the human's nerve-related signals comprises measuring the human's nerve-related signals with an implanted intravascular device.

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