171 results about "Visual evoked potentials" patented technology

Visual or photic stimuli generating methods, devices and control systems for inducing steady-state visual evoked potential (SSVEP) from human viewers without causing discomfort to the viewers or distorting the embedding images are disclosed. The control system includes a stimuli-generating device and an electroencephalography (EEG) sensing device. The stimuli-generating device includes a first and a second light source. The first light source generates a flickering light with a first wavelength while a second light source generates another flickering light with one or more wavelength(s) differ from that of the first one. Together, the light sources generate visual / photic stimuli flickering above their critical flicker fusion threshold while maintaining the colorfulness and hue of the embedding images. At least one electrode of the EEG sensing device is connected to each viewer, configured to receive and analyze his / her EEG signals in order to detect and determine his / her responses to the stimuli.

The invention provides a system and method for controlling a brain computer interface (BCI) based on multimode fusion. The system comprises a brain electrostimulation and feedback module, an electroencephalogram (EEG) signal acquisition module, an EEG signal processing module and an execution module, wherein the brain electrostimulation and feedback module is used for evoking an SSVEP (Steady State Visual Evoked Potential) and inducing an MI (Motor Imagery); the EEG signal acquisition module is used for acquiring EEG signals; the EEG signal processing module is used for extracting, identifying and classifying SSVEP characteristics and MI EEG characteristics in the EEG signals and feeding classified results which respectively correspond to the SSVEP characteristics and the MI EEG characteristics back to the brain electrostimulation and feedback module; and the execution module is used for executing the classified results. According to the system and the method, a multimode fusion BCI is constructed, so that the information transmission rate, reliability and flexibility of a control system are improved, the low information transmission rate of a BCI in a single MI mode is reduced, meanwhile, the visual burden under a single SSVEP task is reduced, and the adaptation crowds of the BIC-based control system are increased.

The invention relates to a robot system based on a brain-computer interface and an implementation method. The robot system is mainly characterized in that a brain-computer interface sub-system obtains a brain electrical signal through a measurement electrode and performs feature extraction of the brain electrical signal to obtain the intention of a user; the brain-computer interface sub-system converts the intention of the user into a control command and transmits the control command to a robot sub-system through a communication sub-system; the robot sub-system receives the control command of the brain-computer interface sub-system and controls a robot to move; the robot sub-system transmits video information acquired by self to the brain-computer interface sub-system; and the brain-computer interface sub-system forms a stimulation interface fed back to the user. According to the invention, mobility control of the robot in eight directions through the steady-state visual evoked potential brain-computer interface can be realized; furthermore, the system can control the robot rapidly and precisely without being trained; because the brain-computer interface is free from body movement participation, the user suffering from severe dyskinesia can move to an expected position; and thus, the living quality is increased.

The invention discloses a brain-controlling animal robot system and a brain-controlling method of an animal robot. A corresponding control instruction is generated by collecting brain electrical signals of the brain and processing the brain electrical signals, the corresponding control instruction is used for controlling the animal robot to move, and a brain-to-brain normal form of two mixed modes can effectively control the brain-to-brain animal robot. The brain-controlling animal robot system and the brain-controlling method of the animal robot adopt two control modes, namely the mixed control mode based on ocular electricity / myoelectricity characteristics and motion imagery characteristics of the brain electrical signals and the mixed control mode based on visual evoked potential characteristics and the motion imagery characteristics, select a proper control mode according to the state of a user, and largely improve the real-time performance and reliability of control. The brain-controlling animal robot system can be applied to the fields of unknown environment exploration, brain function mechanism research, brain-to-brain network communication, life assistance and entertainment for the disabled and the like.

An electroencephalogram interface is activated by utilizing a biological signal, in particular a steady-state visual evoked potential, from a user.An electroencephalogram interface system (1) includes a biological signal detection section (4) for detecting an electroencephalogram signal from a user (3) and a control section (5) for distinguishing a component of an event-related potential which is contained in the electroencephalogram signal and causing a device to operate based on the distinguished event-related potential. The electroencephalogram interface system (1) further includes an activation apparatus (2) for activating the control section (5) and an output section (6) for presenting a visual stimulation which flickers with a predetermined frequency based on a signal from the activation apparatus (2). The activation apparatus (2) includes: a frequency analysis section (7) for analyzing a frequency component of the electroencephalogram signal detected by the biological signal detection section (4), and detecting an intensity corresponding to the predetermined frequency; and a determination section (8) for comparing the detected intensity of the predetermined frequency and a predetermined threshold, and if the intensity of the predetermined frequency is equal to or greater than a predetermined threshold, determining that the user (3) has been paying attention to the visual stimulation and thus activating the control section (5).

The invention discloses an electrocerebral time-frequency component dual positioning normal form quick character input method, which includes the steps: (1) exerting visual stimulation on a time domain and a frequency domain after a character input phase is started; (2) dividing channels of EEG (electroencephalogram) signals acquired in real time into two groups according to time-frequency components including SSVEP (steady-state visual evoked potentials) and P300 so as to perform signal processing respectively while a user pays attention to a target character; (3) judging whether the user executes character input operation or not, determining the target character by means of the step (2) and displaying the target character on a character input result display area if the user is judged to execute character input operation, and if the user is judged not to execute character input operation, shifting to a character input interface rather than performing character input, and then shifting to the step (1) so that the user reselects the target character. The method has the advantages of simplicity in principle, convenience in operation, capabilities of improving target identification accuracy rate and increasing character input speed and the like.

The invention discloses an intelligent wheelchair system based on SSVEP (steady-state visual evoked potential), and aims to solve the problems of high requirements on a user, less control commands and imperfect wheelchair functions of existing device during the process of using electroencephalogram to control a wheelchair. The intelligent wheelchair system comprises an LED strobe stimulator (1), an electrode cap (2), an electroencephalogram pre-processing circuit (3), a wireless transmission system (4), an upper computer (5), a wheelchair control circuit (6), an electric wheelchair (7), and an automatic obstacle avoidance module (8). Electroencephalogram signals collected by the electrode cap (2) are pre-processed through the electroencephalogram pre-processing circuit (3) and then are transmitted to the upper computer (5) through the wireless transmission system (4) to process and generate control signals, and the control signals are transmitted through the wireless transmission system (4) to the wheelchair control circuit (6) to control the wheelchair. The intelligent wheelchair system has the advantages of convenience in operation and safety in use, and can serve as an assisting tool for the old and the disabled.

The application provides a system and a method of cursor control based on a brain-computer interface. The system comprises a visual evoked simulation module, an electroencephalogram acquisition instrument and a processor, wherein the visual evoked simulation module is used for providing P300 evoked potential stimulation and SSVEP (Steady State Visual Evoked Potential) stimulation for a user in an interface display manner; the electroencephalogram acquisition instrument is used for acquiring the electroencephalogram signal of the user in real time and carrying out amplification and analog-to-digital conversion, and carrying out signal transmission through data wires and the processor; and the processor is used for receiving and processing the electroencephalogram signal, and judging the control purpose of the user and controlling cursor movement.

The invention discloses a hybrid brain-computer interface method based on steady state motion visual evoked potential and default stimulation response. The method includes the steps that 1, a testee wears an electrode cap, a reference electrode, a ground electrode and a testing electrode on the electrode cap make contact with the head of the testee, and the vision and the computer screen are in the eye level through visual inspection; 2, a steady state motion visual evoked potential and default stimulation response mixed normal form program is compiled through MATLAB in advance, the testee selects a stimulation target to stare according to a target prompt, and electroencephalogram signals acquired by the electrode cap are stored in a computer; 3, steady state motion visual evoked potential features and default stimulation response features are subjected to feature extraction respectively, and then the stimulation target is subjected to classified recognition; 4, the computer screen displays the stimulation target recognition result, and visual feedback is conducted on the testee; 5, the steps are repeated, and the next round is conducted till the program is ended. According to the hybrid brain-computer interface method, two types of feature recognition information is adopted, and the method has the advantages that operation is simple, less training time is needed, and less electrodes are needed.

The invention belongs to the field of brain electricity and identity identification, providing an identity identification method which can effectively describe the components of the characteristic of the brain electricity and can achieve the requirement for improving the identification rate. The technical scheme is that the identity identification method based on visual evoked potential (VEP) comprises the following steps of: applying some image stimulation to a subject; selecting a proper scalp leading electrode to capture the visual evoked potential of the subject; preprocessing an original brain electricity signal in a de-noising way; extracting a gamma wave band power spectrum to be taken as the characteristic of the brain electricity to be researched; processing the characteristic of the brain electricity with Fisher linear discrimination; and studying and testing the optimized characteristic parameter in a classifying way through a BP neural network to realize the identity identification. The method is mainly applied to the identity identification.

The invention relates to a three-stage brain-controlled upper limb rehabilitation method combining steady-state visual evoked potential and mental imagery (MI). The method comprises the following steps: (1) the first stage of VR video guidance training: a patient is made to be familiar with upper limb rehabilitation movements through VR video guidance; (2) the second stage of VR-SSVEP training: the patient needs to concentrate to observe pictures that represent different upper limb movements and flicker with a specific frequency, EEG signals of the patient are collected in real-time to analyzeintentions of the patient, and visual feedback is provided to the patient through VR animation to make the patient learn to concentrate; and (3) the third stage of VR-MI training: EEG signals of theleft and right upper limbs of the patient during MI are collected during off-line training, and a mental imagery intention recognition model is established. The EEG signals of mental imagery of the patient are analyzed according to the model during online training, movement intentions of the patient are recognized, and movements of a 3D character in an interface are controlled in real time, so that brain central nerve remodeling is facilitated through MI. The method exhibits a good immersion property, enables active rehabilitation to be realized, enables rehabilitation to proceed step by step,and is a new method for upper limb rehabilitation of a cerebral stoke patient.

The invention discloses a two-dimensional cursor motion control system and method based on motor imagery and steady-state visual evoked potential. The system comprises an electrode cap, an electroencephalogram acquisition instrument, a system control unit, a data processing module, a cursor control module and a visual stimulator, wherein the visual stimulator is provided for a user in an interface display way. The method comprises the following steps that the user executes motor imagery and visual attention tasks simultaneously according to a working interface instruction; the electrode cap acquires an electroencephalogram signal; the electroencephalogram acquisition instrument performs amplifying, filtering and analog-digital conversion on the electroencephalogram signal; the system control unit separates electroencephalogram data generated by motor imagery and visual attention and saving; the data processing module performs preprocessing, feature extraction and classification and identification on two kinds of electroencephalogram data in sequence; the cursor control module controls a cursor to perform continuous two-dimensional motion according to a classification and identification result. The system has the advantage of high control accuracy, high robustness, capability of realizing two-dimensional cursor continuous motion and the like, and can be used for performing motion control on a computer mouse.

A steady-state visual evoked potential signal classification method based on a convolutional neural network comprises the following steps: firstly, presenting checkerboard stimuli flipped at differentfrequencies to a user at the same time, and acquiring an electroencephalogram signal when the user watches a specific target by using an electroencephalogram acquisition device; making original multi-channel electroencephalogram signals when the user watches different stimulation targets into a data set with labels, and dividing the data set into a training set, a verification set and a test set;inputting the training set into a designed deep convolutional neural network model for training, performing network optimal parameter selection by using a verification set, and finally inputting thetest set into the trained deep convolutional neural network model to complete the recognition of the stimulation target. Accurate identification of steady-state visual evoked potential signals can beachieved, the characteristic of adaptively extracting signal characteristics is achieved, manual preprocessing is not needed, and meanwhile individual difference can be better adapted through data learning.

The invention discloses a multi-frequency time sequence combined steady-stage visual evoked potential brain-computer interface method. The method comprises the following steps of: putting a measurement electrode on the head of a subject and connecting the measurement electrode with an electroencephalogram amplifier, a computer and a computer screen; and forming n different representation targets by using n stimulus frequencies, wherein n is an integer more than 1; and realizing the representation of the visual targets and the visual feedback of the subject by using the computer screen. The method improves the information transmission rate of the brain-computer interface by constructing a plurality of representation targets from a few stimulus frequencies, has the advantages of multiple representation targets and high specificity of the detection result, can realize communication between the disabled having normal mind but suffering from paralysis of the neuromuscular system and an external apparatus, and is suitable for a normal person on occasions that the limbs are inconvenient to control.

The invention relates to a target selecting method based on transient visual evoked electroencephalogram, comprising the following steps: VC + + writing visual stimulator evokes an electroencephalogram signal, 16-lead collecting device collects an electroencephalogram signal VEP which is amplified by an electroencephalogram amplifier and A / D converted, so that the signal is input into a computer and memorized in a memorizer in a way of signal voltage magnitude; B sample band biorthogonal wavelet method is used for extracting an electroencephalogram characteristic signal, in addition, corresponding results are classified, identified and output by the self-learning ability of BP neuronic network; wherein, the method also comprising the following steps of: designing the accurate timing visual stimulator by CPU timestamp; answering the output impulse of paralled port; collecting the electroencephalogram signal VEP by a collecting device; pretreating the collected signal; extracting the electroencephalogram signal by the B sample band biorthogonal wavelet method; and classifying characteristic quantity by the BP neuronic network. The method has the advantage that the BP neuronic network is used for effectively improving signal to the noise ratio and the recognition rate of visual evoked potential VEP.

The invention relates to an advanced mechanical arm control system based on BCI and an implementation method thereof. The system comprises a machine vision subsystem, a brain-computer interface subsystem and a mechanical arm subsystem; the brain-computer interface subsystem and the machine vision subsystem are connected to the mechanical arm subsystem through a TCP / IP for communication, and the machine vision subsystem recognizes and positions coordinates of a target object in a photographing mode; and the brain-computer interface subsystem is used for acquiring scalp electroencephalogram signals generated by steady-state visual evoked potentials and performing real-time analysis and feature extraction on the scalp electroencephalogram signals, the control intention of a person is online decoded and a computer-recognizable control command is generated, and the machine arm subsystem operates according to the control command. According to the system, the design is reasonable, the controlintention of the person can be decoded online and the computer-recognizable control command is generated to control the mechanical arm to operate, the accurate multi-objective control function is realized, and the system has the characteristics of being safe, efficient and reliable.

The invention discloses a two-dimensional cursor movement control system and method based on motor imagery and coded modulation visual evoked potential. The system comprises an electrode cap, an electroencephalography acquisition instrument, a system control unit, a data processing module, a cursor control module and a visual stimulator, wherein the visual stimulator is provided for a user in an interface display way. The method comprises the following steps that: the user simultaneously executes a motor imagery task and a visual attention task according to a work interface instruction; the electrode cap collects an electroencephalography signal; the electroencephalography acquisition instrument carries out amplification, filtering and analog-digital conversion on the electroencephalography signal; the system control unit separates the electroencephalography data generated by the motor imagery and the visual attention, and then, the electroencephalography data is stored; the data processing module carries out preprocessing, feature extraction and classification identification on two classes of electroencephalography data in sequence; and the cursor control module controls a cursor to carry out continuous two-dimensional movement according to classification identification. The system has the advantages of being high in control accuracy, good in robustness and the like, the continuous movement of a two-dimensional cursor can be realized, and the system can be used for the movement control of a computer mouse.

The invention discloses a deep learning mixed model-based steady state visual evoked potential classification method. The method comprises the steps of 1, adopting an LCD display as a stimulation source, determining a flicker frequency, selecting an electrode channel for electroencephalogram collection, carrying out an experiment for multiple different testees, and performing collection to obtain a steady state visual electroencephalogram signal database; 2, based on short-time-sequence electroencephalogram signals in the database, training and determining parameters of a convolutional neural network model, and finishing automatic extraction of features of the electroencephalogram signals; and 3, adopting an output of a convolutional deep learning network as an input of a Boltzmann machine network, performing fine adjustment on parameters of a classification network model for the different testees, and determining parameters of a Boltzmann machine network model. According to the method, the extraction of the generalization features of the electroencephalogram signals can be well realized; the influence of electroencephalogram signal distortion on signal classification is reduced; and the short-time-length electroencephalogram signals can be utilized to well finish the signal classification.

The invention provides a brain-computer interface based doctor-patient interaction method, which adopts a brain-computer interface based doctor-patient interaction system comprising an electroencephalographic collection module and an electroencephalographic analysis and doctor-patient interaction module. The method comprises the following steps of: setting visual stimulation signals with different frequencies, displaying the signals on a display, and mapping into controls of four commands through different combined codes (00, 01, 10 and 11) of 0 and 1; collecting an electroencephalographic signal of a subject by an electrode in real time, sending into a computer after the signal is amplified; analyzing the electroencephalographic signal; and broadcasting a voice prompt corresponding to the identification result through a speaker, and displaying the result on the display. Therefore, medical staff can carry out corresponding rescue according to the display and voice. The brain state can be identified by the brain-computer interface technology, and external equipment is accurately driven in time, so that communication and control can be realized. Brain can be effectively excited to generate steady state visual evoked potential by visual stimulations in different frequencies, the electroencephalographic signal can be accurately identified under stimulations in different frequencies through signal calculation and processing functions of the computer, and real doctor-patient interaction can be realized in the result display and voice prompt mode.

The invention discloses a steady state visual evoked potential brain-computer interface signal identification method, which comprises the following steps of: (1) simultaneously displaying a plurality of different pictures with different flicker frequencies through a visual stimulation unit and acquiring electroencephalogram signals of a testee who stares at the visual stimulation unit; (2) carrying out noise estimation and noise suppression on the electroencephalogram signals by a data processing unit, and then carrying out characteristic extraction and judgment on the processed electroencephalogram signals to primarily determine the picture at which the testee stares; and (3) upsetting the flicker frequencies of the displayed pictures, acquiring the electroencephalogram signals, then carrying out noise estimation and noise suppression on the currently acquired electroencephalogram signals, then carrying out characteristic extraction and judgment on the processed electroencephalogram signals to determine the picture at which the testee stares, if the currently determined picture is the same as the picture determined in the step 2, taking the picture as finally determined identification information to output, and if not, judging that the testee does not stare at any picture displayed by the visual stimulation unit. According to the steady state visual evoked potential brain-computer interface signal identification method, the electroencephalogram signal identification accuracy can be effectively improved.

Provided is a steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement. First, an electrode is arranged on the head of a user, a detected electroencephalogram signal is sent to a computer, then movement stimulating units are presented in front of the user through a computer screen at the same time, the movement stimulating units are shielded by two-dimensional noise points obeying Gaussian distribution, the noise point update frequency is synchronized with the screen refresh rate, and the noise strength is represented by Gaussian distribution standard deviation. After the movement stimulating units are formed, the user watches any one of the movement stimulating units, a computer acquires a stimulation starting flag bit and a stimulation finishing flag bit, a computer signal is acquired through a test electrode, the significance probabilities of different stimulation targets are calculated, the targets are judged and indicated through the screen, and then the next target identification is conducted. The steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement can remarkably enhance brain response strength of the user, improve precision and efficiency of an existing brain computer interface, and innovatively improve practical level of the brain computer interface technology.

The invention discloses a Hilbert-Huang Transform (HHT)-based high-frequency combined coding steady state visual evoked potential brain-computer interface method, which comprises the following steps of: connecting hardware, expressing different targets by using nn stimulation sequences formed by time sequence permutation and combination through n high-frequency stimulation frequencies, coding the stimulation sequences to form a code base, then executing electroencephalogram data characteristics by adopting a Hilbert-Huang Transform (HHT)-based variable frequency electroencephalogram signal characteristic extraction method, acquiring a Hilbert time-frequency graph of the electroencephalogram data, and comparing the extracted electroencephalogram data characteristics with the code base by using a local frequency spectrum extreme value target identification method to realize quantified target identification accuracy. The method has the advantages of simple operation, little electrode number, more target number, reduction of testee fatigue, and reduction of probability of inducing testee epilepsy.

The present invention belongs to the field of medical electronics, and is especially system and method for separating binocular vision evoked potentials (VEP). The system includes one binocular vision separating device and one multi-focus vision electrophysiological system with double figure stimulator. The double figure stimulator stimulates both the right and the left eyes through the binocular vision separating device to generate mixed electrophysiological signal, which is extracted with the electrodes, amplified with the biosignal amplifier and converted in the data acquisition unit into digital signal fed to the computer for analysis and treatment. By means of fast M conversion, are realized the separation of the VEP in different areas, the separation of the binocular VEP and the design of competitive inhibition ratio index for the diagnosis of monocular amblyopia. The present invention is used in the early diagnosis and curative effect evaluation of monocular amblyopia.

The invention belongs to the technical field of electroencephalogram identification, and provides an identifying method based on visual evoked P3 potential. The technical scheme includes that the identifying method based on visual evoked P3 potential includes steps: using the coherence averaging method to separate P3 signals, wherein recorded signals, evoked potentials and noise are in a relation accordant to xi(n)=s(n)+ni(n), i=1,2,......M and n=1,2,......N(1), the evoked potentials in fixed lock-time relation with stimulation gradually increase during repeated simulation and superposition, and the random background noise is canceled out to separate out P3; and using the separated P3 waveform as characteristics and inputting the characteristics into an SVM (support vector machine) for training and testing, wherein the 10-fold cross validation method is used for improving identification precision. The identifying method based on visual evoked P3 potential is mainly applied to identification places.

The invention relates to the field of medical apparatuses. For realizing the aim of simultaneously evoking the SSVEP (Steady State Visual Evoked Potential) blocking characteristic and the P300 characteristic, and separating the two characteristics in space and frequency domain (wherein the effective combination of the two different characteristic signals can obviously improve the single task judging accuracy of a BCI (Brain-Computer Interface) system), the invention adopts the technical scheme that a brain-computer interface method based on SSVEP blocking and P300 bicharacteristics is provided. The method comprises the following steps of: evoking the bicharacteristics by visual stimulation, acquiring and storing the generated electroencephalogram signal, preprocessing the electroencephalogram signal, extracting corresponding SVEP blocking and P300 characteristic signals, and classifying the signals by linear discriminant analysis, thereby using the characteristics in pattern recognition of experimental tasks. The method provided by the invention is mainly used for design and manufacture of medical apparatuses.

The invention relates to a visual drive method, control method and control system of a brain wave human-computer interface. In the visual drive method, a plurality of flickering light sources are displayed for user observation, and different phase codes are made for the flash time sequence of the flickering light sources by utilizing time delay; and the visual evoked potential of a user is triggered by the flash time sequence for subsequent control, and the purpose of increasing system use comfort is achieved.

A wireless BCI (Brain Computer Interface) input system based on SSVEP (Steady-State Visual Evoked Potentials) brain electric potential comprises a SSVEP keyboard and a brain electric headband. The SSVEP keyboard is used for inducing the SSVEP brain electric potential of a user and comprises buttons flashing according to a specific frequency, wherein a mask for marking a key value can be replaced; the brain electric headband comprises a brain electric acquisition module, a brain electric signal analysis module and a Bluetooth communication module which are respectively used for acquiring a brain electric signal of the user, identifying the SSVEP electric potential to determine an input intention of the user, and sending the key value to a matched mobile intelligent device through Bluetooth; and the brain electric headband is a portable wearable device and, and by combination with the SSVEP keyboard, can implement wireless control on the mobile intelligent device. According to the wireless BCI input system based on the SSVEP brain electric potential, a way of direct interaction between a human brain and the mobile intelligent device is provided, the action of a patient who loses action ability but has healthy thought can be assisted, and a convenient control tool for freeing the hands of healthy people is provided.

The invention discloses a method for a brain-computer interface based on amplitude modulated visual evoked potential. The method comprises the following steps of: firstly, connecting an electroencephalogram signal acquisition system; optimally selecting the most-sensitive frequency of amplitude frequency response of a tested person as carrier frequency; maximizing a signal-noise ratio of a response signal; secondly, providing a selection range of modulation wave frequency, designing an AMVEP (Amplitude Modulated Visual Evoked Potential) normal form and generating a stimulation sequence of an amplitude modulated visual evoked potential brain-computer interface normal form; and finally, realizing the identification of single signal or a small amount of signals of the normal form AMVEP. The invention provides the normal form based on the amplitude modulated visual evoked potential and a method for analysis and characteristic extraction of a modulation signal corresponding to the normal form AMVEP. The identification of single signal or a small amount of signals of the normal form AMVEP based on the amplitude modulated visual evoked potential is realized; and the method disclosed by the invention has the advantages of simpleness in operation, fewer electrodes and more target numbers.

The embodiment of the invention relates to an identity recognition method and device based on brain electrical signals. The method includes the steps of determining a target stimulus frequency sequence, displaying n segments of stimulus signals corresponding to the target stimulus frequency sequence for a to-be-tested user; obtaining n segments of steady sate visual evoked potential (SSVEP) signals generated by the n segments of the stimulus signals; when the similarity between n segments of the SSVEP signals and n segments of preset SSVEP signals is greater than or equal to a threshold value, determining that the identity of the to-be-tested user is correct; if not, determining that the identity of the to-be-tested user is wrong. According to the identity recognition method and device based on the brain electrical signals, identity recognition is conducted through the SSVEP signals; compared with existing brain electrical identity recognition methods, stimulus duration can be shortened, signal features are more stable, an identity recognition system constructed on the basis of the identity recognition method and device is higher in confidentiality and uneasy to duplicate and counterfeit.

The invention relates to the field of medical equipment in order to improve the transmission rate of system information greatly. The invention adopts the technical scheme that the multi-brain-computer interface method for three characteristics of SSVEP (Steady State Visual Evoked Potential), blocking and P300 comprises the following steps: adopting a computer and an electroencephalogram acquisition system including an electroencephalogram electrode, an electroencephalogram amplifier and an electroencephalogram filter, wherein the computer further comprises an EEG (Electroencephalo-Graph) analytical program and a stimulation and induction user interface; acquiring electroencephalogram data of multiple channels by using the electroencephalogram acquisition system; generating an electroencephalogram signal from cerebral cortex, detecting the electroencephalogram signal by using the electroencephalogram electrode, amplifying by using the electroencephalogram amplifier, and filtering and inputting to the computer; and carrying out subsequent data process on the acquired electroencephalogram data to extract corresponding SSVEP, SSVEP blocking and P300 characteristic signals, thus the characteristics are applied to mode recognition of experimental tasks. The multi-brain-computer interface method is mainly applied to the design and manufacturing of the medical equipment.