JP2019109888A - Double closed circuit brain-machine interface system and method therefor - Google Patents

Abstract

To improve the accuracy of control on devices using brain signals, for example, robot arm control.SOLUTION: The double closed circuit brain-machine interface includes: a stage S320 of generating motion information based on a motion intention and generating a control signal for controlling an external device when a brain signal corresponding to the motion intention of a user is sensed; a stage S326 of acquiring a sensory signal corresponding to an operation state of the external device and analyzing the sensory signal to obtain a brain stimulation pattern for enabling the user to recognize the operation state of the external device; and a stage S340 of analyzing the motion intention brain signal to generate information on a brain stimulation frequency which stimulates a brain region of the user, correcting the brain stimulation pattern using the information on the brain stimulation frequency, and giving assistance by referring to the corrected brain stimulation pattern to stimulate the brain region through the brain stimulated region.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a dual closed circuit brain-machine interface system and method thereof, and more particularly, (a) when an exercise intention brain signal, which is a brain signal corresponding to a user's exercise intention, is sensed, The machine interface system analyzes the exercise intention brain signal to generate exercise information which is information on the exercise based on the exercise intention of the user; (b) the brain-machine interface system corresponds to the exercise information A control signal for controlling an external device operating in place of the body part of the user, and assisting the external device to operate in response to the control signal; A sensory signal corresponding to an operating state is acquired, and the sensory signal is analyzed to allow the user to recognize the operating state of the external device. Brain stimulation pattern (the brain stimulation pattern induces the somatic sensation corresponding to the somatosensory information set according to the operation state to enable the user to recognize the operation state of the external device Acquiring a brain site set to stimulate the patient's brain); and (c) the brain-machine interface system analyzes the exercise intention brain signal to determine the user's brain site. The information on the frequency of stimulating brain stimulation is generated, the information on the frequency of brain stimulation is used to correct the brain stimulation pattern, and the brain region is stimulated through the brain stimulation unit with reference to the corrected brain stimulation pattern A dual closed circuit brain-machine interface method and system comprising the steps of:

  Brain-machine interface (BMI) technology is a technology that controls external electronic devices or devices simply by thinking through mutual communication and analysis of brain and machine. More specifically, it is a technique of analyzing an intention based on brain signals related to the user's intention and controlling an external thing or a virtual object.

  In the existing study, it was reported that the robot arm could be controlled to some extent simply by inserting an invasive microelectrode into the primary motor cortex, occipital cortex etc of the quadriplegia patient and based on the measured brain signal Sometimes.

  And, in the case of brain-machine interface technology using brain cortical electroencephalogram (ECoG) in which the size of the electrode is larger than that of the fine electrode, although control accuracy is somewhat inferior to a system using the fine electrode, It has the advantage of being able to cover a larger brain area without destruction.

  However, while the limitations on the performance of brain signal based brain-machine interface systems are also clear, researchers point out that they can not receive somatosensory feedback while performing motor commands.

  In addition, even if somatic sensory feedback is received, there is a problem that it is not possible to restore the inter-brain region feedback mechanism which has a great effect on actual motor performance only by simply using a feedback system through brain stimulation.

  The present invention aims to solve all the above-mentioned problems.

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

  Another object of the present invention is to induce the inter-brain region feedback for brain signals generated in association with exercise intention to the user with a brain signal sensing / brain stimulation sequence.

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

  The characteristic configurations of the present invention for achieving the object of the present invention as described above and realizing the characteristic effects of the present invention described later are as follows.

  According to one aspect of the present invention, in the method of interfacing between a user's brain and machine, (a) when an exercise intention brain signal that is a brain signal corresponding to the user's exercise intention is sensed, the brain The machine interface system analyzes the exercise intention brain signal to generate exercise information which is information on the exercise based on the exercise intention of the user; (b) the brain-machine interface system corresponds to the exercise information A control signal for controlling an external device operating in place of the body part of the user, and assisting the external device to operate in response to the control signal; A sensory signal corresponding to an operating state is acquired, and the sensory signal is analyzed to enable the user to recognize the operating state of the external device. Stimulation pattern (The brain stimulation pattern induces the somatosensory information corresponding to the somatosensory information set according to the operation state in order to enable the user to recognize the operation state of the external device Acquiring a brain site set in (a); and (c) the brain-machine interface system analyzes the exercise intention brain signal to stimulate the user's brain site. The information on brain stimulation frequency is generated, the information on brain stimulation frequency is used to correct the brain stimulation pattern, and the brain stimulation unit is stimulated with reference to the corrected brain stimulation pattern. Providing a double closed circuit brain-machine interface method comprising:

  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 the user's exercise intention. A process of analyzing the movement intention brain signal to generate movement information which is information for movement based on the movement intention of the user, when the movement intention brain signal which is a signal is sensed, (2) corresponding to the movement information A control signal for controlling an external device operating in place of the body part of the user, and assisting the external device to operate in response to the control signal; A brain stimulation pattern for acquiring a sensory signal corresponding to an operating state and analyzing the sensory signal to enable the user to recognize the operating state of the external device The stimulation pattern corresponds to the somatosensory information set according to the operation state to enable the user to recognize the operation state of the external device, and the brain is set to induce the somatosensory sense. A process for acquiring a site stimulation pattern, and (3) analyzing the exercise intention brain signal to generate information on brain stimulation frequency for stimulating the user's brain site, the brain stimulation frequency The processor is configured to perform a process of correcting the brain stimulation pattern using information on the basis of the information, and referring to the corrected brain stimulation pattern to support stimulation of the brain region through the brain stimulation unit. A dual closed circuit brain-machine interface system is provided.

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

  The present invention provides somatic sensory feedback to the user through external device control through the user's motion-related brain signal decoding and brain stimulation corresponding to sensory signals e.g. Have the effect of inducing

  In addition, there is an effect of inducing inter-brain region feedback to brain signals generated in association with exercise intention to the user with a brain signal sensing / brain stimulation sequence.

  In addition, the present invention has the effect of minimizing the noise of brain signals generated by brain stimulation.

FIG. 1 is a schematic view of a double closed circuit brain-machine interface system and an external device according to an embodiment of the present invention. FIG. 2 is a schematic diagram of a brain-machine interface of a user according to an embodiment of the present invention. FIG. 3 is a schematic view of 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 schematic view of a noise removing unit according to an embodiment of the present invention. FIG. 5 illustrates a schematic process of removing noise of 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 show, by way of illustration, 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 should be understood that the various embodiments of the invention, although different from one another, 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 according to one embodiment. Also, it should be understood that the location or arrangement of the individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention, as properly set forth, includes all the equivalent scope of what that claim claims: It is only limited by the claims appended hereto. Like reference symbols in the drawings refer to the same or similar functionality in several 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 practice the present invention. To be.

  FIG. 1 is a view schematically showing 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 stimulation unit 150 and a noise removal unit 160. obtain.

  The processor 110 of the dual closed circuit brain-machine interface system 100 assists in providing or providing feedback to the user on the user's motor intention brain signal, which will be described in more detail later in the detailed description. I assume.

  The communication unit 120 may be realized by various communication techniques. That is, Wi-Fi (WIFI: registered trademark), WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), HSPA (High Speed Packet Access), Mobile WiMAX (Mobile WiMAX) : Registered trademark, WiBro (registered trademark), LTE (Long Term Evolution), Bluetooth (bluetooth (registered trademark), infrared communication (IrDA, infrared data association), NFC (Near Field Communication), Zigbee (Zigbee: registered Trademark, wireless LAN Surgery, etc. can be applied. Also, when providing a service in connection with the Internet, the Internet may be based on TCP / IP, which is a standard protocol for information transmission.

  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. Also, in some cases, the processor 110 may transmit and receive necessary information with the external device 400 through 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 sense types. Pattern of brain reaction, magnitude of stimulation for inducing the brain response, stimulation time, frequency, information on these spatiotemporal changes and position information of the brain signal sensing unit 140 for sensing the brain signal of the user And position information of the brain stimulation unit 150 for stimulating the user's brain.

  At this time, the database 130 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, an SD or XD memory), a RAM (a memory type). Random Access Memory (RAM), SRAM (Static Random Access Memory), ROM (Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), PROM (Programmable Read Only Memory), magnetic memory, magnetic disk, optical disk 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. In addition, the database 130 may be installed inside the double closed circuit brain-machine interface system 100 or may be separately installed from the double closed circuit brain-machine interface system 100 so that the double closed circuit brain-machine is installed. Data may be transmitted or received through communication with the interface system 100.

  The sensing unit 140 senses a brain signal of the user, 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 brain signal sensing of 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 the user's brain region.

  Next, the stimulation unit 150 is a part that stimulates the user's brain, and the stimulation may be electrical stimulation, and may be applied simultaneously or with some time lag in multiple channels. And, the stimulation time may be fluid depending on the type of stimulation, and may range from several milliseconds to around 1 second. In addition, the stimulation unit 150 can stimulate the brain in an invasive manner or can stimulate the brain in a non-invasive manner, and the primary or secondary somatosensory cortex and the posterior parietal cortex in the user's brain region. Or it may be located in a part of the pre-motion area or the like.

  Next, the noise removal unit 160 removes noise from the actually measured signal in real time after estimating a noise signal that can be actually measured in the brain environment based on physical characteristics of brain stimulation applied to the user's brain However, this will be described in detail later through 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 part 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 all devices capable of driving according to a user's brain signal.

  The sensor unit 420 is a part that acquires a sense signal generated by driving the external device 400, and may include, but not limited to, a pressure sensor, a position sensor, a vibration sensor, a temperature sensor, etc. All sensors that can be measured can be included.

  Referring to FIG. 2, a method for interfacing between a user's brain and a machine using the dual closed circuit brain-machine interface system 100 and the external device 400 according to one embodiment of the present invention configured as described above will be described. The explanation is as follows.

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

  Such sensed movement intention related brain signals may pass through the signal amplifier of the sensing unit 140, an analog filter, an analog-to-digital converter, etc., and the passed signals are processed by the processor 110 of the double closed circuit brain-machine interface system 100. It may go through preprocessing such as digital filtering, re-referencing, nerve spike signal sensing, firing rate calculation and so on.

  Then, the processor 110 of the dual closed circuit brain-machine interface system 100 may generate motion information, which is 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 motion information may be an artificial neural network algorithm, but is not limited thereto, and may be a Bayesian classifier, a regression type machine learning algorithm, or the like. At least a part of all algorithms conforming to the purpose of generating motion information which is motion prediction information using brain signals may be used.

  Also, the processor 110 of the double closed circuit brain-machine interface system 100 may generate a control signal for controlling an external device 400 which operates in place of the body part of the user in response to the motion 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 double closed circuit brain-machine interface system 100 may obtain the sense signal generated by the operation state of the drive unit 410 through the sensor unit 420 (S240).

  Next, the processor 110 of the dual closed circuit brain-machine interface system 100 elicits a somatic sensation for analyzing the acquired sensory signals so that the user can recognize the operating state of the external device 400. To generate a brain stimulation pattern for stimulating the set brain site (S250).

  That is, the processor 110 detects brain response patterns corresponding to a plurality of sensory types stored in the database 130, magnitudes of stimuli for inducing the brain responses, stimulation times, frequencies, and their spatio Information on dynamic changes may be received through the communication unit 120 and may be compared with each other to the acquired sensory signal to generate a brain stimulation pattern for inducing a most similar brain response corresponding to the sensory signal.

  Also, brain stimulation patterns may differ depending on the type and size of sensory signals, spatio-temporal changes, etc., and machine learning or deep learning algorithms may be used in the process of generating brain stimulation patterns.

  Steps S210 to S250 of the dual closed circuit brain-machine interface system 100 as described above may be further embodied with reference to FIG. 3, but referring to FIG. 3, (i) exercise intention related brain signal sensing (S310). ), (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) may be performed by a brain-external device-brain feedback loop.

  Next, the processor 110 of the dual closed circuit brain-machine interface system 100 may analyze exercise intention related brain signals sensed by the sensing unit 140 to determine exercise intention time and exercise intention strength (S 260).

  At this time, judgment of exercise intention time and intensity may utilize brain signals of a cerebral pre-motion area, a movement area, an occipital cortex, a frontal lobe, and the like.

  As an example, in the case of brain cortical EEG, existing preparatory potential-based methods are also used, but event-related desynchronization (ERD, about 7 to 20 Hz) and high-frequency high-gamma prior to movement. Brain activity patterns in (High-gamma, 50 Hz and higher) regions may be used. Further, in the case of a fine electrode, the firing timing and the firing rate degree become a basic measure in exercise intention, time point and strength judgment, but the connectivity strength between the brain regions can also be a main reference. This is the same as in the case of brain cortex EEG. At this time, connectivity analysis may be other than simultaneous connectivity analysis methods such as phase-amplitude coupling, phase synchronization, classical correlation analysis, mutual information, etc. Causal connectivity analysis such as partial directed coherence can also be used.

  First, the point in time of exercise intention is calculated by calculating the point in time when the fluctuation of brain signal to brain signal of the resting state in which the processor 110 does not clearly indicate the exercise intention exceeds the standard such as 2 standard deviations (SD). It may be obtained. However, in this case, since there is a high possibility that a false alarm occurs, brain signal patterns before and after the time can be analyzed to determine the time.

  For example, if a motion exceeding 2 SD is consistently observed after the time while showing brain signal swing similar to the resting state before the time, the probability that this time is the exercise intention time is Use an algorithm that makes use of the fact that it is higher and determines that "If there are 2 SD or more trials for 1 second or more, then this time is the exercise intention time" or if you use Bayesian-based machine learning A method or the like can be used, and at least a part of all the methods meeting the purpose can be used without being limited to this and it can be determined that “this point is the exercise intention point in a probabilistic manner”.

  And, the strength of the exercise intention is a pause state where the exercise intention signal and the exercise intention are clearly not based on the brain signal when the exercise intention time point is determined by the processor 110 and the subsequent additional brain signals. It can be judged 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 the brain stimulation time point corresponding to the exercise intention time point to analyze the exercise intention strength to analyze the user's brain region. Information on the frequency of brain stimulation that stimulates the brain (S270).

  For example, it is possible to generate brain stimulation frequency by substituting exercise intention strength with a distribution function in which the stimulation frequency is at the maximum per unit time, that is, asymptote exists, and not limited to this, the exercise intention strength determines the brain stimulation frequency Can be generated using at least a part of all the methods that meet the purpose of

  Steps S210, S260 and S270 of the dual closed circuit brain-machine interface system 100 as described above may be more embodied with reference to FIG. 3, but referring to FIG. 3, (i) motion intention related brain signal sensing The inter-brain region feedback loop may be implemented by (S310), (ii) exercise intention time point and strength judgment (S330), and (iii) trigger information for brain stimulation time point and information generation for brain stimulation frequency (S322).

  Next, the processor 110 of the dual closed circuit brain-machine interface system 100 can 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 may be converted into the signal with reference to the generated brain stimulation pattern to assist the stimulation unit 150 to stimulate the user's brain region (S290).

  At this time, referring to FIG. 3, the dual closed circuit brain-machine interface system 100 integrates feedback information generated in the brain-external device-brain feedback loop and feedback information generated in the inter-brain area feedback loop. After correcting the brain stimulation pattern, brain stimulation that induces somatic sensation may be applied with the corrected brain stimulation pattern (S340).

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

  As one example, the processor 110 of the dual closed circuit brain-machine interface system 100 is shown in FIG. 2 as (i) motion intention brain signal sensing of the user (S210), motion information generation (S220), external device control Brain-external device-brain feedback shown in FIG. 3 corresponding to the sequence of signal generation, external device operation (S230), external device operation state corresponding sensory signal acquisition (S240) and brain stimulation pattern acquisition (S250) ii) shown in FIG. 3 corresponding to the user's motion intention brain signal sensing (S210), motion intention time point and strength judgment (S260), trigger information for brain stimulation time point and information generation for brain stimulation frequency (S270) order Interbrain feedback may be performed simultaneously.

  Also, as another example, the processor 110 of the double closed circuit brain-machine interface system 100 is shown in FIG. 2 as (i) motion intention brain signal sensing of the user (S210), exercise intention time point and strength judgment. (S260), after performing the inter-brain region feedback shown in FIG. 3 corresponding to the trigger information for the brain stimulation time point and the information generation for the brain stimulation frequency (S270), (ii) the user's motion intention brain Corresponds to the order of signal sensing (S210), motion information generation (S220), external device control signal generation, external device operation (S230), sensory signal acquisition (S240) corresponding to external device operation state (B) and brain stimulation pattern acquisition (S250) The brain-external device-brain feedback shown in FIG. 3 may also be performed.

  Meanwhile, brain stimulation for somatosensory induction is intermittently applied using the dual closed circuit brain-machine interface system 100 to intensively stimulate brain stimulation when motion intention can be sensed through brain signal decoding. If it can be applied and does not try to exercise any more, it can return to the intermittent brain stimulation sequence again.

  Then, the double closed circuit brain-machine interface system 100 can change the brain stimulation frequency according to the characteristics of the motion area signal (for example, the motion intention strength) and the intensity of the sensory sensor signal even after the motion intention detection. More frequent sensory feedback can be performed, especially when delicate movements have to be performed, such as when the sensory related brain signals have different intensities depending on the degree to which the user concentrates.

  In addition, when the dual closed circuit brain-machine interface system 100 increases the external sensory sensor signal by a certain level or more even if the motor intention is not detected in the resting state (the state where the brain stimulation is intermittently applied) For example, the brain stimulation frequency may be increased if a person or a device contacts a robot arm).

  FIG. 4 is a schematic view of the noise removal unit 160 of the double closed circuit brain-machine interface system 100 according to an embodiment of the present invention.

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

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

  A method of removing noise in the double closed circuit brain-machine interface system 100 according to an embodiment of the present invention will be described with reference to FIG. 5 as follows.

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

  In addition, the modeling unit 162 of the noise removal unit 160 may acquire the acquired position information of the brain stimulation 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 user who has been saved. The modeling data may be used to model a noise signal pattern that can be sensed by the brain signal sensing unit 140 by the brain site stimulation of the user through the brain stimulation unit 150 (S520).

  At this time, a lead-field matrix-based forward modeling technique generally used in brain science may be applied to the calculation for modeling the noise signal pattern, and this is because the brain It can also be applied when the stimulation channel is multi-channel.

  Then, the signal processing unit 164 of the noise removal unit 160 may receive an exercise intention brain signal including noise due to brain stimulation sensed by the brain signal sensing unit 140 (S530).

  Next, the signal processing unit 164 of the noise removal unit 160 compares the motion intention brain signal including the noise with the modeled noise signal pattern to obtain a noise signal pattern modeled with the motion intention brain signal. The matching noise signal may be removed (S 540).

  At this time, methods such as real-time independent component analysis (ICA) and principal component analysis (Principal Component Analysis) may be used as a method of removing a noise signal, and modeling is performed with each extracted component. After the correlation with the noise signal is calculated using Pearson correlation or Mutual Information analysis, the signal may be reconstructed excluding the component with the highest value.

  On the other hand, the reconstructed signal from which the noise signal has been removed may be used for motion information generation for the user's motion intention, motion intention point determination and motion intention strength determination.

  The embodiments of the present invention described above may be implemented in the form of program instructions that may be implemented through various computer components and may be recorded on a computer readable recording medium. The computer readable recording medium may include program instructions, data files, data structures and the like singly or in combination. The program instructions stored in the computer readable recording medium may be specially designed and configured for the present invention, or may be known to those skilled in the computer software art. . Examples of computer readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs, DVDs, magnetic-optical devices such as floppy disks. Included are media (magneto-optical media) and hardware devices specially configured to store and execute program instructions such as ROM, RAM, flash memory and the like. Examples of program instruction words include not only machine language code such as that produced by a compiler, but also high-level language code 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 the processing according to the present invention, and vice versa.

  Although the present invention has been described by specific items such as specific components, etc., and by limited examples and drawings, it is provided to assist in a more general understanding of the present invention. The present invention is not limited to the above-described embodiment, and various modifications and variations can be made from the description by those skilled in the art to which the present invention belongs.

  Therefore, the spirit of the present invention should not be limited to the embodiments described above, and not only the claims described below, but all variations equivalent or equivalent to the claims of the present invention. The thing of the present invention belongs 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: stimulation unit 160: noise removal unit 162: modeling unit 164: signal processing unit 400: external device 410: drive unit 420: sensor Department

Claims (14)

In the method of interfacing between the user's brain and machine,
(A) When an exercise intention brain signal, which is a brain signal corresponding to the user's exercise intention, is sensed, a brain-machine interface system analyzes the exercise intention brain signal and performs exercise based on the user's exercise intention. Generating exercise information that is information for
(B) the brain-machine interface system generates a control signal for controlling an external device which operates in place of the body part of the user in response to the motion information, and corresponds to the control signal The external device is supported to operate to obtain a sensory signal corresponding to the operating state of the external device, and the sensory signal is analyzed to allow the user to recognize the operating state of the external device. Stimulation pattern (the brain stimulation pattern corresponds to the somatosensory information corresponding to the somatosensory information set according to the operation state in order to enable the user to recognize the operation state of the external device Acquiring a pattern to stimulate a brain site set to induce); and (c) said brain-machine interface system is configured to: Analysis information to generate information on the brain stimulation frequency for stimulating the user's brain region, correcting the brain stimulation pattern using the information on the brain stimulation frequency, and referring to the corrected brain stimulation pattern Assisting to stimulate the brain site through a brain stimulation unit;
A double closed circuit brain-machine interface method comprising:   (D) the brain-machine interface system (i) first position information of the brain signal sensing unit for sensing the user's exercise intention brain signal, (ii) brain stimulation for stimulating the brain region (Iii) movement intention brain signal of the user sensed in the step (a) with reference to the second position information of the part and (iii) the corrected brain stimulation pattern acquired in the step (c) Removing the noise signal, wherein the second position information is located within a predetermined distance from the first position information, and the user's set brain region is received through the second position information. The double closed circuit brain-machine interface method according to claim 1, wherein a brain stimulation signal that stimulates the movement intention brain signal acquired through the first position information is affected. In removing the noise signal,
(D-1) The brain-machine interface system includes: first position information of the brain signal sensing unit; second position information of the brain stimulation unit; the corrected brain stimulation pattern; and the stored user. Modeling noise signal patterns corresponding to noise signals that can be sensed by the brain signal sensing unit by the user's brain region stimulation through the brain stimulation unit with reference to three-dimensional brain structure modeling data;
(D-2) comparing the modeled noise signal pattern with the motion intention brain signal sensed by the brain signal sensing unit and comparing the noise signal pattern matched with the modeled noise signal pattern with the motion intention brain signal A method as claimed in claim 2, characterized in that it is eliminated.   In the step (b), the brain-machine interface system generates brain response patterns respectively corresponding to a plurality of sensory types that can be sensed by the user, and a magnitude of a stimulus for inducing the brain response. Generating the brain stimulation pattern for inducing a brain response corresponding to the sensory signal with reference to a database in which at least one of stimulation time, frequency and information on these spatiotemporal changes is stored. The double closed circuit brain-machine interface method according to claim 1, characterized in that:   In the step (c), the brain-machine interface system generates trigger information for analyzing the exercise intention brain signal to determine a time to stimulate the set brain region of the user, and the trigger information is generated. The double closed circuit brain-machine interface method according to claim 1, wherein the brain stimulation pattern in the step (b) is additionally used to correct the brain stimulation pattern.   The brain-machine interface system detects the moment when the user's exercise intention brain signal exceeds a predetermined threshold as compared to the resting brain signal corresponding to the user's absence of exercise intention. The double closure according to claim 5, characterized in that trigger information is generated which is determined as an exercise intention time point and which determines a time point to stimulate the set brain region of the user corresponding to the exercise intention time point. Circuit brain-machine interface method.   In the step (c), the brain-machine interface system generates an exercise intention brain signal of the user after the exercise intention brain signal of the user and the exercise intention brain signal of the user, and the exercise intention brain signal of the user A method of analyzing the brain intention of the user by comparing it with a resting brain signal corresponding to a no state to judge the user's intention to exercise and analyzing the exercise intention to generate information on the brain stimulation frequency. The double closed circuit brain-machine interface method according to claim 1. In a brain-machine interface system for interfacing between the user's brain-machine,
The communication intention accessible to the external device, and (1) an exercise intention brain signal which is a brain signal corresponding to the exercise intention of the user is sensed, the exercise intention brain signal is analyzed to analyze the exercise intention of the user A process of generating exercise information which is information for exercise based on (2) generating a control signal for controlling an external device operating in place of the body part of the user corresponding to the exercise information, Assisting the external device to operate in response to a control signal to obtain a sensory signal corresponding to the operating state of the external device, and analyzing the sensory signal to determine the operating state of the external device as the user Stimulation pattern to allow the user to recognize (the brain stimulation pattern corresponds to somatosensory information set according to the operation state to enable the user to recognize the operation state of the external device Somatic feeling A process of acquiring a pattern to stimulate a brain site set to induce a sense of mind, and (3) a frequency of brain stimulation that analyzes the exercise intention brain signal and stimulates the user's brain site A process of generating information on the brain, correcting the brain stimulation pattern using the information on the brain stimulation frequency, and assisting the brain region to be stimulated through the brain stimulation unit with reference to the corrected brain stimulation pattern A double closed circuit brain-machine interface system comprising a processor for performing   (I) first position information of a brain signal sensing unit for sensing the user's exercise intention brain signal by the processor; (ii) second position information of a brain stimulation unit for stimulating the brain region; (Iii) A process of removing noise signals included in the user's motion intention brain signal sensed by the processor (1) with reference to the corrected brain stimulation pattern acquired by the processor (3). And the second position information is located within a predetermined distance from the first position information, and a brain stimulation signal for stimulating the user's set brain region through the second position information is 9. The dual closed circuit brain-machine interface system according to claim 8, wherein the exercise intention brain signal acquired through one position information is affected. 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 3-dimensional brain structure modeling data of the user. A process of modeling a noise signal pattern corresponding to a noise signal that can be sensed by the brain signal sensing unit by the brain site stimulation of the user through the brain stimulation unit, and the modeled noise signal pattern and the brain signal sensing The exercise intention brain signal according to claim 9, wherein the exercise intention brain signal is compared to perform a process of removing a noise signal corresponding to the modeled noise signal pattern in the exercise intention brain signal. Double closed circuit brain-machine interface system.   (2) in the process, the processor detects brain responses corresponding respectively to a plurality of sensory types that the user can sense, a magnitude of stimulation for inducing the brain response, a stimulation time, and a frequency And generating a brain stimulation pattern for inducing a brain response corresponding to the sensory signal with reference to a database in which at least one of information on these spatiotemporal changes is stored. Item 9. The double closed circuit brain-machine interface system according to item 8.   In the (3) process, the processor generates trigger information for analyzing the exercise intention brain signal to determine a time point to stimulate the user's set brain region, and additionally using the trigger information. 9. The double closed circuit brain-machine interface system according to claim 8, wherein the brain stimulation pattern in the (2) process is corrected.   The processor determines a moment at which the user's exercise intention time point is a moment at which the user's exercise intention brain signal exceeds a predetermined threshold value as opposed to a resting brain signal corresponding to a state in which the user does not exercise intention. The double closed circuit brain-machine according to claim 12, wherein trigger information is generated to determine and determine a time to stimulate the user's set brain region corresponding to the exercise intention time. Interface system.   In the (3) process, the processor handles the user's motion intention brain signal and the user's additional motion intention brain signal sensed after the motion intention brain signal to the state where the user's motion intention is absent. 9. The method according to claim 8, wherein the intentional exercise strength of the user is determined by comparing with the brain signal of the resting state to judge the exercise intention strength of the user, and the exercise intention strength is analyzed to generate information on the brain stimulation frequency. Double closed circuit brain-machine interface system.