JP6649462B2 - Dual closed circuit brain-machine interface system and method - Google Patents

Description

  The present invention relates to a dual closed-circuit brain-machine interface system and method, and more particularly, (a) when a movement intention brain signal, which is a brain signal corresponding to a user's movement intention, is detected. A step in which the machine interface system analyzes the movement intention brain signal to generate movement information that is information on movement based on the movement intention of the user; (b) the brain-machine interface system corresponds to the movement information. Generating a control signal for controlling an external device that operates in place of the user's body part, and assists the external device to operate in response to the control signal; A sensory signal corresponding to an operation state is acquired, and the sensory signal is analyzed so that the user can recognize the operation state of the external device. Brain stimulation pattern (the brain stimulation pattern induces the somatic sensation corresponding to the somatosensory information set for each operation state so that the user can recognize the operation state of the external device) (C) the brain-machine interface system analyzes the motion-intention brain signal to determine the user's brain region. Generating information on a brain stimulation frequency to be stimulated, correcting the brain stimulation pattern using the information on the brain stimulation frequency, and stimulating the brain region through a brain stimulation unit with reference to the corrected brain stimulation pattern. And a method and system for dual closed circuit brain-machine interface.

  Brain-Machine Interface (BMI) technology is a technology for controlling an external electronic device or device only by thinking through mutual information transmission and analysis between the brain and the machine. More specifically, it is a technique of analyzing an intention based on a brain signal related to a user's intention and controlling an external thing or a virtual object.

  An existing study reported that inserting an invasive microelectrode into the primary motor cortex, occipital parietal cortex, etc. of a patient with quadriplegia could control the robot arm to some extent simply by thinking based on measured brain signals. Sometimes.

  In the case of the brain-machine interface technology using the brain cortical electroencephalogram (ECoG) in which the size of the electrode is larger than that of the microelectrode, the control accuracy is somewhat inferior to the system using the microelectrode, but the brain tissue is almost completely removed. It has the advantage that it can cover a larger brain area without destroying it.

  However, although the limitations on the performance of the brain signal-based brain-machine interface system are clear, researchers have cited the main reason for this inability to receive somatosensory feedback during motor command execution.

  Further, even if somatic sensory feedback is received, there is a problem in that a feedback mechanism between brain regions that has a great effect on motor performance can not be restored by simply using a feedback system through brain stimulation.

  An object of the present invention is to solve all the problems described above.

  In addition, the present invention provides control of an external device through decoding of a motion-related brain signal of the user and physical stimulation of the user through brain stimulation corresponding to a sensory signal (for example, a sensor signal of the external device) induced in the control process. Another purpose is to induce sensory feedback.

  It is another object of the present invention to induce a user to perform inter-brain region feedback on a brain signal generated in association with a movement intention through a brain signal sensing / brain stimulation sequence.

  Another object of the present invention is to minimize noise of brain signals generated by brain stimulation.

  The characteristic configuration of the present invention for achieving the above object of the present invention and realizing the characteristic effects of the present invention described below is as follows.

  According to one aspect of the present invention, there is provided a method for interfacing between a user's brain and a machine, comprising the steps of: (a) detecting a brain intention signal that is a brain signal corresponding to the user's movement intention; A step in which the machine interface system analyzes the movement intention brain signal to generate movement information that is information on movement based on the movement intention of the user; (b) the brain-machine interface system corresponds to the movement information. Generating a control signal for controlling an external device that operates in place of the user's body part, and assists the external device to operate in response to the control signal; Acquiring a sensory signal corresponding to an operation state, analyzing the sensory signal, and enabling the user to recognize an operation state of the external device. Stimulation pattern (The brain stimulation pattern is for inducing the somatosensory according to the somatosensory information set for each operation state so that the user can recognize the operation state of the external device. And (c) the brain-machine interface system analyzes the motion intention brain signal and stimulates the user's brain region. Generating information on a brain stimulation frequency, correcting the brain stimulation pattern using the information on the brain stimulation frequency, and stimulating the brain region through a brain stimulation unit with reference to the corrected brain stimulation pattern. Providing a double closed circuit brain-machine interface method.

  According to another aspect of the present invention, in a brain-machine interface system for interfacing between a user's brain and a machine, a communication unit accessible to an external device, and (1) a brain corresponding to a user's exercise intention. A process of analyzing the motion intention brain signal when the motion intention brain signal is sensed to generate motion information that is information on motion based on the motion intention of the user; (2) corresponding to the motion information Generating a control signal for controlling an external device that operates in place of the user's body part, and assists the external device to operate in response to the control signal; A brain stimulation pattern for acquiring a sensory signal corresponding to an operation state, analyzing the sensory signal, and enabling the user to recognize an operation state of the external device (the brain stimulation pattern). The stimulation pattern corresponds to the somatosensory information set for each operation state so that the user can recognize the operation state of the external device, and a brain set to induce the somatosensory. (3) analyzing the brain signal of the intention to exercise to generate information on the frequency of brain stimulation for stimulating the brain part of the user, and A processor for performing a process of correcting the brain stimulation pattern using information on the brain stimulation pattern and supporting the brain part through a brain stimulation unit with reference to the corrected brain stimulation pattern. Providing a dual closed circuit brain-machine interface system.

  According to the present invention, the following effects can be obtained.

  The present invention provides somatic sensory feedback to a user through control of an external device through decoding of a motion-related brain signal of the user and brain stimulation corresponding to a sensory signal (eg, a sensor signal of the external device) induced in the control process. Has the effect of inducing.

  In addition, there is an effect that the inter-brain area feedback for the brain signal generated in association with the exercise intention is guided to the user by the brain signal sensing / brain stimulation sequence.

  Further, the present invention has an effect of minimizing noise of a brain signal generated by brain stimulation.

FIG. 1 is a diagram schematically illustrating a double closed circuit brain-machine interface system and external devices according to an embodiment of the present invention. FIG. 2 illustrates a schematic process of interfacing between a user's brain and a machine according to an embodiment of the present invention. FIG. 3 is a diagram schematically illustrating a double closed circuit brain-machine interface system having a double feedback loop according to an embodiment of the present invention. FIG. 4 is a diagram schematically illustrating a noise removing unit according to an embodiment of the present invention. FIG. 5 is a schematic diagram illustrating a process of removing noise from a user's brain signal according to an embodiment of the present invention.

  The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of example, specific embodiments in which the invention may be practiced. These examples are described in sufficient detail to enable those skilled in the art to practice the invention. It is to be understood that various embodiments of the invention are different from one another, but need not be mutually exclusive. For example, the particular shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in one embodiment. It should also be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the present invention. Therefore, the following detailed description is not intended to be construed in a limiting sense, and the scope of the invention, when properly described, is intended to cover all scopes equivalent to those claimed in the following claims: It is limited only by the appended claims. In the drawings, like reference numbers indicate identical or similar functions in some aspects.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention belongs can easily carry out the present invention. It shall be.

  FIG. 1 is a diagram schematically illustrating a double closed circuit brain-machine interface system 100 and an external device 400 according to an embodiment of the present invention.

  First, as shown in FIG. 1, the double closed circuit brain-machine interface system 100 of the present invention includes a processor 110, a communication unit 120, a database 130, a sensing unit 140, a stimulating unit 150, and a noise removing unit 160. obtain.

  The processor 110 of the dual closed-circuit brain-machine interface system 100 provides or assists the user in providing feedback to the user's motor intention brain signal, which will be described in greater detail below. And

  The communication unit 120 can be realized by various communication technologies. That is, Wi-Fi (WIFI: registered trademark), WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), HSPAX Mobile (HXApX) : Registered trademark), WiBro (registered trademark), LTE (Long Term Evolution), Bluetooth (registered trademark), infrared communication (IrDA, infrared data association), NFC (Near Field Communication: Negative Communications, NFC (Near Field Communication)) Trademark), wireless LAN Surgery, etc. can be applied. Also, when a service is provided by being connected to the Internet, it may be based on TCP / IP, which is a standard protocol for transmitting information on the Internet.

  The processor 110 of the dual closed-circuit brain-machine interface system 100 may receive necessary information from the database 130 through the communication unit 120. In some cases, the processor 110 may transmit and receive necessary information to and from the external device 400 via the communication unit 120.

  The database 130 of the double closed-circuit brain-machine interface system 100 stores various data for the operation of the double closed-circuit brain-machine interface system 100, and corresponds to a plurality of sensory types. Brain response pattern, magnitude of stimulation for inducing the brain response, stimulation time, frequency, information on these spatio-temporal changes, and position information of the brain signal sensing unit 140 for sensing a user's brain signal And position information of the brain stimulating unit 150 for stimulating the user's brain.

  At this time, the database 130 may include a flash memory type (flash memory type), a hard disk type (hard disk type), a multimedia card micro type (multimedia card micro type), a card type memory (for example, an SD or XD memory), and a RAM (RAM). Random Access Memory (RAM), SRAM (Static Random Access Memory), ROM (Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), EEPROM (Electrically Reasonable Programmable Read Only Memory) And obtained contain at least one type of storage medium of the click may include any medium capable of storing data not limited to this. Further, the database 130 may be installed inside the double closed circuit brain-machine interface system 100, or alternatively, may be installed separately from the double closed circuit brain-machine interface system 100 to provide the double closed circuit brain-machine interface. Data may be transmitted or received through communication with the interface system 100 and recorded.

  Next, the sensing unit 140 is a unit that senses a user's brain signal, and may include at least a part of a brain signal sensor, a signal amplifier, an analog filter, an analog-digital converter, and the like. The sensing of the brain signal by the sensing unit 140 may be an invasive method or a non-invasive method, and the sensing unit 140 may be located in a primary motor cortex region of a user's brain region.

  Next, the stimulating unit 150 is a part that applies a stimulus to the user's brain, and the stimulus may be an electrical stimulus, and may be applied simultaneously or with a certain time lag on multiple channels. The stimulus time is fluid depending on the type of stimulus, and may have a range from a few milliseconds to within or outside one second. In addition, the stimulating unit 150 may stimulate the brain in an invasive manner or stimulate the brain in a non-invasive manner, and the primary or secondary somatosensory cortex, the occipital parietal cortex among the brain regions of the user. Or it may be located, for example, in a part of the pre-motion area.

  Next, the noise removing unit 160 estimates a noise signal that can be measured in the actual brain environment based on physical characteristics of brain stimulation applied to the user's brain, and then removes noise in real time from the actually measured signal. However, this will be described in detail later in the detailed description.

  Meanwhile, as shown in FIG. 1, the external device 400 may include a driving unit 410 and a sensor unit 420.

  The driving unit 410 is a unit that is driven by an external device according to a user's brain signal and may be a robot arm, but is not limited thereto, and may include any device that can be driven by a user's brain signal.

  The sensor unit 420 is a unit that acquires a sensory signal generated by driving the external device 400, and may include a pressure sensor, a position sensor, a vibration sensor, a temperature sensor, and the like, but is not limited thereto. All sensors that can measure can be included.

  A method for interfacing between a user's brain and a machine using the double closed circuit brain-machine interface system 100 and the external device 400 according to an embodiment of the present invention will now be described with reference to FIG. It is as follows.

  First, a brain signal related to a user's movement intention among the user's brain signals may be detected by the sensing unit 140 of the double closed circuit brain-machine interface system 100. At this time, the sensing unit 140 may sense a user's movement intention brain signal using an invasive or non-invasive brain signal sensor (S210).

  The sensed movement intention-related brain signal may pass through a signal amplifier, an analog filter, an analog-to-digital converter, and the like of the sensing unit 140, and the passed signal is processed by the processor 110 of the dual closed circuit brain-machine interface system 100. It may go through a pre-processing process such as digital filtering, re-referencing, nerve spike signal sensing, and firing rate calculation.

  Then, the processor 110 of the dual closed circuit brain-machine interface system 100 may generate motion information as motion prediction information based on the motion intention of the user using the sensed brain signal (S220). At this time, an algorithm used to generate the motion information may be an artificial neural network algorithm, but is not limited thereto. For example, a Bayesian classifier, a regression type machine learning algorithm, or the like. At least a part of all algorithms corresponding to the purpose of generating motion information as motion prediction information using the brain signal may be used.

  Also, the processor 110 of the dual closed-circuit brain-machine interface system 100 may generate a control signal for controlling the external device 400 that operates in place of the body part of the user according to the exercise information. According to the control signal, the driving unit 410 of the external device 400 may operate as intended by the user (S230).

  Next, the processor 110 of the dual closed circuit brain-machine interface system 100 may acquire a sensory signal generated according to an operation state of the driving unit 410 through the sensor unit 420 (S240).

  Next, the processor 110 of the dual closed-circuit brain-machine interface system 100 analyzes the acquired sensory signal to induce a somatic sensation for the user to recognize the operation state of the external device 400. A brain stimulation pattern for stimulating the brain region set for the purpose may be generated (S250).

  That is, the processor 110 may determine a brain response pattern corresponding to each of a plurality of sensory types stored in the database 130, a magnitude of a stimulus for inducing the brain response, a stimulus time, a frequency, The information about the target change may be received through the communication unit 120 and may be compared with the acquired sensory signal to generate a brain stimulation pattern for inducing a most similar brain response corresponding to the sensory signal.

  Also, the brain stimulation pattern may vary depending on the type and magnitude of the sensory signal, spatio-temporal changes, and the like, and machine learning and deep learning algorithms may be used in the process of generating the brain stimulation pattern.

  Steps S210 to S250 of the dual closed circuit brain-machine interface system 100 as described above may be more specifically embodied with reference to FIG. 3, but with reference to FIG. ), (Ii) motion information generation, control signal generation and transmission (S320), (iii) three-dimensional robot arm / hand control (S322), (iv) sensory signal transmission (S324), and (v) brain stimulation pattern generation ( S326) can be performed by the brain-external device-brain feedback loop.

  Next, the processor 110 of the dual closed circuit brain-machine interface system 100 may analyze the motion intention related brain signal detected by the sensing unit 140 to determine a motion intention time and a motion intention intensity (S260).

  At this time, the determination of the time point and the intensity of the movement intention may use the brain signals of the premotor area, the motion area, the occipital parietal cortex, the frontal lobe, etc. of the cerebrum.

  For example, in the case of the cortical EEG, the existing preparatory potential-based method is also used. Brain activity patterns in the region (High-gamma, 50 Hz or higher) can be used. Further, in the case of a fine electrode, the firing timing and the firing rate are the basic measures in determining the intention of exercise, the time point, and the intensity, and the strength of connectivity between the brain regions may be the main reference. This is the same in the case of cortical EEG. At this time, the connectivity analysis may be performed by a method other than a simultaneous connectivity analysis method such as phase-amplitude coupling, phase synchronization, classical correlation analysis, and mutual information. Causal connectivity analysis, such as partial directed coherence, can also be used.

  First, the processor 110 determines the time point of the exercise intention by calculating the time point at which the fluctuation of the brain signal in the resting state where the exercise intention is not clear exceeds the standard such as 2 standard deviations (SD). May be obtained. However, in this case, since a false alarm is likely to occur, the time point can be determined by analyzing a brain signal pattern before and after the time point.

  For example, if the brain signal fluctuations similar to the resting state are shown before the time point and the fluctuations exceeding 2SD are consistently observed after the time point, the probability that this time point is the exercise intention time point is low. An algorithm is used to judge whether or not the number of trials is equal to or more than 2SD for one second after the time, and that this time is a motion intention time, or Bayesian-based machine learning A method or the like can be used, and the present invention is not limited to this, and at least a part of all methods that meet the purpose that can be determined stochastically that this time is the exercise intention time can be used.

  The intensity of the exercise intention is determined by the processor 110 when the exercise intention time is determined and the brain intention signal based on the brain signal at the time when the exercise intention time is determined and the additional brain signal thereafter. The determination can be made based on the distance value between the peak values on the X-axis of the brain signal.

  Next, the processor 110 of the dual closed-circuit brain-machine interface system 100 may generate trigger information for a brain stimulation time point corresponding to the movement intention time, analyze the movement intention strength, and analyze the user's brain region. May be generated for the brain stimulation frequency for stimulating (S270).

  For example, it is possible to generate a brain stimulation frequency by substituting the exercise intention intensity into a distribution function having a maximum stimulation frequency per unit time, that is, an asymptote, and the exercise intention intensity is not limited thereto, and the brain intention frequency determines the brain stimulation frequency. It can be generated using at least a part of all methods that meet the purpose of doing so.

  Steps S210, S260 and S270 of the dual closed circuit brain-machine interface system 100 as described above can be further embodied with reference to FIG. 3. This can be performed by a feedback loop between brain regions consisting of (S310), (ii) determination of exercise intention time and intensity (S330), and (iii) generation of trigger information on brain stimulation time and information on brain stimulation frequency (S322).

  Next, the processor 110 of the double closed circuit brain-machine interface system 100 may correct the brain stimulation pattern with reference to the trigger information for the brain stimulation time point and the information for the brain stimulation frequency (S280). The brain stimulation pattern is converted into the signal with reference to the brain stimulation pattern, and the stimulation unit 150 may be used to assist the user in stimulating a brain part of the user (S290).

  At this time, referring to FIG. 3, the double closed circuit brain-machine interface system 100 integrates the feedback information generated by the brain-external device-brain feedback loop and the feedback information generated by the inter-brain region feedback loop. Then, after correcting the brain stimulation pattern, a brain stimulation for inducing somatic sensation with the corrected brain stimulation pattern may be applied (S340).

  The method of interfacing between the user's brain and the machine as described above uses the dual closed-circuit brain-machine interface system 100 and (i) the external device 400 using the user's movement intention brain signal. A brain-external device that enables the user to recognize the sensory signal induced in the process of controlling the brain through brain stimulation, and (ii) trigger information for brain stimulation corresponding to the brain signal corresponding to the movement intention of the user; This is a method of integrating feedback between brain regions that generates information on the frequency of brain stimulation through the processor 110, and may be performed by the processor 110 of the brain-machine interface system 100 in the order shown in FIG. However, the present invention is not limited thereto, and may be performed in various orders.

  As an example, the processor 110 of the dual closed-circuit brain-machine interface system 100 includes (i) sensing a user's movement intention brain signal (S210), generating movement information (S220), and controlling external devices shown in FIG. The brain-external device-brain feedback shown in FIG. 3 corresponding to the order of signal generation, external device operation (S230), sensory signal acquisition corresponding to the external device operation state (S240), and brain stimulation pattern acquisition (S250) ( ii) FIG. 3 corresponding to the order of sensing the brain signal of the user's intention to exercise (S210), judging the point of exercise intention and intensity (S260), generating trigger information for the point of brain stimulation and generating information for the frequency of brain stimulation (S270). Inter-brain feedback may be performed simultaneously.

  Also, as another example, the processor 110 of the double-closed-circuit brain-machine interface system 100 may include: (i) sensing the user's intention brain signal (S210); (S260), after performing the inter-brain region feedback shown in FIG. 3 corresponding to the order of generating the trigger information for the brain stimulation time and the information for the brain stimulation frequency (S270), (ii) the user's movement intention brain Signal sensing (S210), exercise information generation (S220), external device control signal generation, external device operation (S230), acquisition of sensory signals corresponding to external device operation status (S240), and acquisition of brain stimulation pattern (S250) 3 may perform the brain-external device-brain feedback shown in FIG.

  On the other hand, when the brain stimulation for somatosensory induction is intermittently applied using the dual closed-circuit brain-machine interface system 100 and the movement intention can be sensed through brain signal decoding, the brain stimulation is intensively performed. If it can be applied and it is not going to move anymore, it can return to the intermittent brain stimulation sequence again.

  In addition, the dual closed-circuit brain-machine interface system 100 may change the frequency of brain stimulation according to the characteristics of the motion area signal (eg, the intensity of the motion intention) and the intensity of the sensory sensor signal even after the detection of the motion intention. Sensory-related brain signals may have more frequent sensory feedback, especially when elaborate movements have to be performed, such as the intensity of the sensory signal depending on the degree of concentration of the user.

  In addition, the dual closed-circuit brain-machine interface system 100 may operate when the external sensation sensor signal increases above a certain level even in a rest state (a state in which brain stimulation is applied intermittently) and no movement intention is sensed ( For example, if a person or a device makes contact with the robot arm), the frequency of brain stimulation may increase.

  FIG. 4 is a diagram schematically illustrating the noise removing unit 160 of the dual closed circuit brain-machine interface system 100 according to an embodiment of the present invention.

  As shown in FIG. 4, the noise removing unit 160 may include a modeling unit 162 and a signal processing unit 164.

  In addition, the noise removing unit 160 may include MRI information-based brain structure modeling data of the user (not shown).

  A method of removing noise of the double closed circuit brain-machine interface system 100 according to an embodiment of the present invention will now be described with reference to FIG.

  First, the modeling unit 162 of the noise removing unit 160 corrects the brain corrected with reference to the position information of the brain stimulating unit 150, the position information of the brain signal sensing unit 140, the information on the brain stimulation frequency, and the like stored in the database 130. Information on the stimulus pattern may be obtained through the communication unit 120 (S510).

  In addition, the modeling unit 162 of the noise removing unit 160 may obtain the acquired position information of the brain stimulating unit 150, the position information of the brain signal sensing unit 140, the corrected brain stimulation pattern, and the MRI information-based brain structure of the saved user. Using the modeling data, a noise signal pattern that can be sensed by the brain signal sensing unit 140 according to the user's brain region stimulation through the brain stimulation unit 150 can be modeled (S520).

  At this time, a calculation of modeling a noise signal pattern may be performed by using a magnetic field derivation matrix (forward-modeling) based forward modeling technique commonly used in brain science. It can also be applied when the stimulation channel is multi-channel.

  In addition, the signal processing unit 164 of the noise removing unit 160 may receive a motion intention brain signal including noise caused by brain stimulation detected by the brain signal sensing unit 140 (S530).

  Next, the signal processing unit 164 of the noise removing unit 160 compares the motion intention brain signal including the noise with the modeled noise signal pattern, and compares the noise signal pattern modeled with the motion intention brain signal with the noise signal pattern. A matching noise signal may be removed (S540).

  At this time, as a method of removing a noise signal, a method such as real-time independent component analysis (ICA) and principal component analysis (Principal Component Analysis) may be used, and each extracted component is modeled and modeled. After calculating the correlation with the noise signal using Pearson Correlation or Mutual Information analysis, the signal having the highest value may be excluded to reconstruct the signal.

  On the other hand, the reconstructed signal from which the noise signal has been removed may be used to generate exercise information on the exercise intention of the user, determine the exercise intention time, and determine the exercise intention intensity.

  The embodiments of the present invention described above can be implemented in the form of program commands that can be executed through various computer components and can be recorded on a computer-readable recording medium. The computer readable recording medium may include a program command, a data file, a data structure, or the like, alone or in combination. The program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable by those skilled in the computer software field. . Examples of a computer-readable recording medium include a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape; an optical recording medium such as a CD-ROM and a DVD; and a magnetic-optical medium such as a floppy disk. A hardware device specially configured to store and execute program commands, such as a medium-optical media and a ROM, a RAM, a flash memory, etc., is included. Examples of program instructions include not only machine language codes such as those produced by compilers, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

  Although the present invention has been described with reference to specific embodiments such as specific components and limited embodiments and drawings, it is provided to facilitate a more general understanding of the present invention. However, the present invention is not limited to the above-described embodiment, and various modifications and variations can be made by those skilled in the art to which the present invention pertains.

  Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and is limited not only to the claims described below but also to any equivalent or equivalently modified claims. Can be said to belong to the category of the idea of the present invention.

100: brain-machine interface system 110: processor 120: communication unit 130: database 140: sensing unit 150: stimulating unit 160: noise removing unit 162: modeling unit 164: signal processing unit 400: external device 410: driving unit 420: sensor Department

Claims (14)

In a method of interfacing between a user's brain and a machine,
(A) When a movement intention brain signal, which is a brain signal corresponding to the movement intention of the user, is detected, a brain-machine interface system analyzes the movement intention brain signal and performs movement based on the movement intention of the user. Generating athletic information that is information for
(B) the brain-machine interface system generates a control signal for controlling an external device that operates in place of the body part of the user in response to the exercise information; Assisting the external device to operate, obtaining a sensory signal corresponding to the operation state of the external device, analyzing the sensory signal so that the user can recognize the operation state of the external device. Brain stimulation pattern (the brain stimulation pattern corresponds to the somatosensory information set for each operation state so that the user can recognize the operation state of the external device, and (C) stimulating a brain region set to induce the stimulus; and (c) the brain-machine interface system uses the motion-intention brain signal. Analyzing the signal to generate information on a brain stimulation frequency for stimulating a brain part of the user, using the information on the brain stimulation frequency to correct the brain stimulation pattern, and refer to the corrected brain stimulation pattern. Supporting the brain part through a brain stimulating unit to stimulate the brain part;
A double closed circuit brain-machine interface method comprising:   (D) the brain-machine interface system: (i) first position information of a brain signal sensing unit for sensing a brain signal intended to exercise the user; (ii) brain stimulation for stimulating the brain region Referring to the second position information of the unit and (iii) the corrected brain stimulation pattern acquired in step (c), the brain signal included in the movement intention brain signal of the user sensed in step (a). Removing the noise signal that is present, wherein the second location information is located within a preset distance from the first location information, and the user's set brain region is determined through the second location information. The brain closed-machine interface method according to claim 1, wherein a brain stimulation signal for stimulating the brain influences the movement intention brain signal obtained through the first position information. In removing the noise signal,
(D-1) The brain-machine interface system may include a first position information of the brain signal sensing unit, a second position information of the brain stimulating unit, the corrected brain stimulation pattern, and a stored user's saved information. Referring to the three-dimensional brain structure modeling data, modeling a noise signal pattern corresponding to a noise signal that can be sensed by the brain signal sensing unit by stimulating a brain part of the user through the brain stimulation unit;
(D-2) comparing the modeled noise signal pattern with the motion intention brain signal detected by the brain signal sensing unit to generate a noise signal that matches the modeled noise signal pattern with the motion intention brain signal. 3. The method as claimed in claim 2, wherein the method comprises eliminating.   In the step (b), the brain-machine interface system may include a brain response pattern corresponding to each of a plurality of types of sensations that can be sensed by the user, a magnitude of a stimulus for inducing the brain response, Generating a brain stimulation pattern for inducing a brain response corresponding to the sensory signal by referring to a database in which at least one of stimulation time, frequency, and information on these spatiotemporal changes is stored. The method of claim 1, wherein the method is a dual closed circuit brain-machine interface.   In the step (c), the brain-machine interface system analyzes the brain signal of the intention to move to generate trigger information for determining a time point for stimulating a set brain part of the user, and generates the trigger information. The method of claim 1, wherein the brain stimulation pattern in step (b) is additionally used.   The brain-machine interface system detects an instant at which the movement intention brain signal of the user exceeds a predetermined threshold value in comparison with a rest state brain signal corresponding to a state where the user does not have movement intention. 6. The double closing device according to claim 5, wherein it is determined that the exercise is intended, and trigger information is determined to determine a time at which the user sets a brain part set in accordance with the exercise intention. Circuit Brain-Machine Interface Method.   In the step (c), the brain-machine interface system determines the motion intention brain signal of the user and the additional motion intention brain signal of the user detected after the motion intention brain signal as the motion intention of the user. The method according to claim 1, further comprising comparing the brain signal in a resting state corresponding to the absence state to determine the exercise intention intensity of the user, and analyzing the exercise intention intensity to generate information on the brain stimulation frequency. 2. The method of claim 1 wherein the brain-machine interface is a double closed circuit. In a brain-machine interface system for interfacing between a user's brain-machine,
A communication unit accessible to an external device; and (1) when a movement intention brain signal, which is a brain signal corresponding to the movement intention of the user, is sensed, the movement intention brain signal is analyzed to analyze the movement intention brain signal. (2) generating a control signal for controlling an external device that operates in place of the body part of the user in accordance with the exercise information; Assisting the external device to operate in response to a control signal, acquiring a sensory signal corresponding to the operation state of the external device, analyzing the sensory signal, and determining the operation state of the external device by the user. A brain stimulation pattern for allowing the user to recognize the brain stimulation pattern (corresponding to the somatosensory information set for each operation state so that the user can recognize the operation state of the external device. Somatic feeling (3) a brain stimulation frequency for analyzing the movement intention brain signal to stimulate the user's brain region. A process of generating information on the brain stimulation pattern, using the information on the brain stimulation frequency to correct the brain stimulation pattern, and referring to the corrected brain stimulation pattern to assist in stimulating the brain region through a brain stimulation unit. A closed-circuit brain-machine interface system comprising a processor for performing   The processor is configured to: (i) first position information of a brain signal sensing unit for sensing a brain signal intended to exercise the user; (ii) second position information of a brain stimulation unit for stimulating the brain region; (Iii) a process of removing a noise signal included in the motion intention brain signal of the user detected by the processor with reference to the corrected brain stimulation pattern acquired by the processor; And wherein the second position information is located within a preset distance from the first position information, and a brain stimulation signal for stimulating a brain region set by the user through the second position information is a second signal. 9. The dual closed-circuit brain-machine interface system according to claim 8, wherein the one-position information affects the movement intention brain signal. In removing the noise signal,
The processor refers to the first position information of the brain signal sensing unit, the second position information of the brain stimulation unit, the corrected brain stimulation pattern, and the stored three-dimensional brain structure modeling data of the user. Modeling a noise signal pattern corresponding to a noise signal that can be sensed by the brain signal sensing unit through the brain stimulation unit through the brain stimulation of the user, and the modeled noise signal pattern and the brain signal sensing The method of claim 9, further comprising: performing a process of comparing the motion intention brain signal detected by the unit to remove a noise signal that matches the modeled noise signal pattern in the motion intention brain signal. Double closed circuit brain-machine interface system.   In the process (2), the processor may include a plurality of brain reaction patterns corresponding to a plurality of types of sensations that can be sensed by the user, a magnitude of a stimulus for inducing the brain reaction, a stimulus time, and a frequency. And generating a brain stimulation pattern for inducing a brain response corresponding to the sensory signal by referring to a database in which at least one of information on the spatiotemporal change is stored. Item 10. A double closed circuit brain-machine interface system according to item 8.   In the process (3), the processor analyzes the movement intention brain signal to generate trigger information for determining a time point for stimulating a brain region set by the user, and additionally uses the trigger information. 9. The double closed-circuit brain-machine interface system according to claim 8, wherein the brain stimulation pattern in the process (2) is corrected.   The processor sets the moment when the motion intention brain signal of the user exceeds a predetermined threshold as compared with a dormant brain signal corresponding to a state where the user does not have motion intention with the motion intention time of the user. The double closed-circuit brain-machine according to claim 12, wherein the determination is performed to generate trigger information for determining a time point for stimulating the set brain part of the user according to the time point of the exercise intention. Interface system.   In the process (3), the processor may be configured to convert the user's motion intention brain signal and the user's additional motion intention brain signal detected after the user's motion intention brain signal into a state where the user does not have a motion intention. 9. The apparatus according to claim 8, wherein the exercise intention intensity of the user is determined by comparing and analyzing with a brain signal in a resting state, and the information about the brain stimulation frequency is generated by analyzing the exercise intention intensity. Double closed circuit brain-machine interface system.