110 results about "Eeg signal analysis" patented technology

The invention relates to a character input device based on eye tracking and P300 electrical potential of the brain electricity, which comprises a video camera array, an eye tracking module, a keyboard module, a brain electricity signal analyzing module and a system control module. The device combines the high spatial resolution of the eye tracking and the high time resolution of the P300 electrical potential detection of the brain electricity, firstly, determines an area where the keyboard is according to visual lines of a user, and then controls random flickers of all character keys in the area, arouses the P300 electrical potential of the brain electricity of the user, determines the character keys which the user wants to select according to the generation time of the electrical potential and the visual line location, thus realizing the input operation of characters. The invention provides a new human-machine interaction technique which does not need manpower to be involved and is not affected by the noise of the environment for use.

An encephalic electrode individualization locating method based on multimode medical image data fusion relates to the fields of medical instruments and medical image processing and neuroimaging. By means of multimode medical image data including magnetic resonance images, X-ray images and intranperative optical photos, the space position relation of an encephalic electrode and a brain tissue structure is comprehensively measured, a relation between the position of the encephalic electrode and a brain anatomical structure is established, and accurate and fast individualization locating of the encephalic electrode is achieved. The encephalic electrode individualization locating method based on multimode medical image data fusion can be used in the fields of cognitive neuroscience basic research, clinic neurosciences and the like, provide accurate brain space position information for an encephalic electroencephalogram signal, and enhance application value in the aspect of electrophysiological basis research.

The invention discloses a method and a device for analyzing brain wave signals, which comprises steps of: (1) collecting original brain wave signals; (2) removing artifacts or noises in the original brain wave signals; (3) phase-space reconstructing of the preprocessed brain wave signals for obtaining a recurrence plot; (4) quantitative analyzing of the recurrence plot for calculating assured value DET of the recurrence plot; wherein, the artifacts in the original brain wave signals are removed by a mixed method of wavelet transformation and empirical mode decomposition. The phase-space reconstructing reconstructs phase-space vector by a method of delaying time so as to obtain the recurrence plot and calculate the ratio of recursion points on diagonal structure of the recurrence plot to all recursion points for obtaining the assured value DET. The method and the device provide good quantified indexes for doctors and accurate and real-time changing situation of depth of anesthesia during the process of the anesthesia, or are used for researching and analyzing change of brain functions, situation of sedation and analgesis, change of a cognizing function, etc.

The present invention relates to a brain-machine interface system based on face recognition specific wave N170 component, which implements sorting of facial pictures and object pictures, and comprises a picture stimulator, a cortical electric signal collector, a face recognition specific wave N170 detector, and a picture sorter. Pictures to be sorted are presented by the visual picture stimulator as visual stimulations to a user, and the cortical electric signals of the user generated in response are recorded and subjected to amplification and A / D conversion, and then processed and analyzed by the face recognition specific wave N170 detector, whereby a determination is made whether the collected cortical electric signals contain specific wave N170 component related to facial picture stimulation, and accordingly, the cortical electric signals are transformed into picture sorting control commands for identifying facial pictures and object pictures. The advantages of the present invention are: the present invention utilizes the specific cortical electric signal N170 component generated in the facial picture recognition process in response to facial picture stimulations, and employs an effective online feature extraction and sorting algorithm in the cortical electric signal analyzer; therefore, the discrimination ratio of the system is increased. The present invention provides a novel means for persons who suffer from dyskinesia but can think normally to communicate with and control the external environment.

The invention, which belongs to the technical field of fatigue driving detection, relates to a monitoring and early-warning method and system for fatigue driving. The system is composed of an EEG signal acquisition and analysis module, an acceleration acquisition module, a main control module, a wireless transmission module and an early warning module. An EEG signal and an acceleration signal are collected simultaneously; the EEG signal acquisition and analysis module processes and calculates the EEG signal and sends an attention feature value and winking intensity information to the main control module; the main control module processes and calculates acceleration data to obtain a head motion situation; according to the three parameters of the attention feature value, winking intensity information, and head motion, the main control module determines whether a drive is in a fatigue state; if so, a fatigue level is graded based on the parameter values; the early warning module carries out corresponding early warning in acoustic, optical, electric modes based on the fatigue level; and the wireless transmission module can transmit the driving state of the driver to an intelligent terminal, a vehicle-mounted network, and a cloud terminal. According to the invention, on the basis of combination of the EEG signal and the acceleration signal, fatigue driving is monitored in a combined way. Therefore, the detection success rate is high; the early warning ways are diversified; the feedback mechanism is effective; and because of combination with the internet of things, the good practical effect is realized.

The invention discloses an electroencephalogram signal analysis method and an electroencephalogram signal analysis device to identify the brain state. The method comprises the following steps of: segmenting the acquired electroencephalogram signal; analyzing the state of each segment of electroencephalogram signal and preprocessing to remove interference; and calculating an ordering entropy of each segment of electroencephalogram signal, and determining the activity state of the brain according to ordering entropy values. The invention can be used for analyzing electroencephalogram signals under the conditions of different cognitive function activities of the brain and physiological statues, and can be particularly applied to studying the changes of brain activities in sleep and the like. Starting from a phase space, ordering entropies are adopted for statistical analysis of the structural change modes of the electroencephalogram signals to disclose the uncertainty, stable degree and information content of the electroencephalogram signals.

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 relates to a portable active electroencephalogram monitor and a control method thereof, belonging to the field of medical apparatus and instruments. The electroencephalogram monitor comprises an electroencephalogram signal acquisition unit, an electroencephalogram signal analysis and storage unit and an upper computer, wherein an input and output end of the electroencephalogram signal acquisition unit is connected with a first input and output end of the electroencephalogram signal analysis and storage unit, and a first digital signal input and output end of the electroencephalogram signal analysis and storage unit is connected with an input and output end of the upper computer. The portable active electroencephalogram monitor has high stability and reliability and can be used for realizing the active long-term monitoring of accidental, transient, paroxysmal or characteristic electroencephalogram activities, and the normal life, study and work of patients can not be influenced. Because long-time electroencephalogram data can be continuously recorded, the electroencephalogram monitor has important clinic value in diagnosing epilepsia and cerebrovascular diseases and studying and analyzing sleep.

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 an attentiveness training and evaluation device based on force tactile-sensation feedback and electroencephalic signal analysis. The device includes a force tactile-sensation training unit, an electroencephalic recording unit and a control unit. The force tactile-sensation training unit is used to send finger touch force data of a trainee to the control unit. The electroencephalic recording unit is used to send electroencephalic signals of the trainee in a training process to the control unit. The control unit includes a training module, an evaluation module and a control module. The training module is used to store training modes. The evaluation module evaluates data fed back by the force tactile-sensation training unit and the electroencephalic recording unit. Thecontrol module adaptively adjusts training difficulty according to an evaluation result. A physiological index and a behavioral index which characterize an attentiveness level are obtained at the same time, the attentiveness level of the subject can be improved in a short term, an anastomosis degree between the obtained physiological index and behavioral index is very high, and the attentivenesslevel of the trainee can be reliably analyzed and evaluated.

In recent years, a great advancement on the electroencephalogram detection and classification feature extraction technology is obtained, various behavior activities of a person are detected from epilepsy morbidities, in a traditional mode, an electroencephalogram is used for authority brain disease studying and definite diagnosis making, and a classification data resource is used for judging human thinking activities and limb, face and eye activities at present; analysis of the electroencephalogram is more and more important in the fields. The invention provides a new electroencephalogram analysis method; and through analysis on the electroencephalogram, on the basis that the classification accuracy is guaranteed, the reversible analysis method is provided. The method can be finally used for definite diagnosis making of clinical experience, the human brain state stage can be judged, and difficulties of electroencephalogram accurate quantitative analysis are avoided; meanwhile, reverse analysis on quantitative analysis results can be carried out, verification with the clinical medicine experience is finally achieved, and more scientific conclusions are obtained

The embodiment of the invention discloses a method and device for extracting the features of an electroencephalogram signal. The method includes the following steps that each to-be-measured electroencephalogram signal is subjected to de-noising, and an effective electroencephalogram signal corresponding to each to-be-measured electroencephalogram signal is obtained; the energy distribution state of each effective electroencephalogram signal, the signal amplitude distribution change state, the LzC complexity and / or the AR model coefficient are extracted, the initial characteristic value of eachto-be-measured electroencephalogram signal is obtained; the obtained initial characteristic value is subjected to characteristic cleaning, characteristic selection and / or characteristic transformation, the effective characteristic value of each to-be-measured electroencephalogram signal is obtained, effective characteristic values are rapidly and effectively extracted out from the to-be-measuredelectroencephalogram signals, and strong technical support is provided for the implementation and development of the electroencephalogram signal technology, so that the accuracy of the electroencephalogram signal analysis is improved.

The invention discloses a method for automatically removing muscle artifacts in a single-channel EEG signal. The method includes the steps that the EEG signal is subjected to SSA decomposition to obtain P signal components; the P signal components are spliced into a P-dimension data matrix by row; the P-dimension data matrix is subjected to time delay processing to obtain several data matrixes; the several data matrixes are subjected to blind source separation with MCCA to obtain a source estimation matrix S and a hybrid matrix A; sources related to muscle artifacts in the source estimation matrix are recognized; muscle artifacts in the source estimation matrix are removed, the sources recognized to be muscle artifacts are set to be zero to obtain a source estimation matrix S' with muscleartifacts eliminated, and a multi-channel EEG signal X'=A*S' with muscle artifacts removed is obtained through reconstruction; all rows of the multi-channel EEG signal X' are summated, and a single-channel EEG signal x' with muscle artifacts removed can be finally obtained. By means of the method, EEG information is retained as far as possible while the muscle artifacts are removed, and thus the accuracy of EEG signal analysis is improved.

The invention discloses a student class listening attention evaluation method based on electroencephalogram analysis. In order to solve the problem that the student class listening attention concentration degree is hard to express, the student class listening attention evaluation method based on electroencephalogram analysis comprises the steps of 1, collecting electroencephalograms of a student,wherein original electroencephalograms are collected, front first-class amplification treatment is performed on the original electroencephalograms, the electroencephalograms obtained after first-classamplification treatment are amplified again, and the amplified electroencephalograms are converted into digital signals; 2, analyzing the electroencephalograms, wherein working frequency interferenceof the electroencephalogram is removed, low-pass filtering treatment is performed on the electroencephalograms, ocular artifacts are removed, feature extraction and quantification are performed, anda sample entropy is obtained as the attention concentration degree; 3, sending the quantified attention concentration degree through a wireless sending device; 4, receiving the attention concentrationdegree data through a wireless receiving device; 5, storing the concentration degree data within a period of time; 6, showing the data through a visualized interface.

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 method for producing cerebral scalp potential graph relates to a formation method of potential information graph, and includes the following steps: according to the electroencephalic original data collected from different lead electrodes on the head portion and non-linear linetic theory utilizing phase space reconstitution technique to form scalp potential information transmission matrix between leads, then plotting the scalp topological distribution graph-electroencephalic information map (BIM) according to the information transmission quantity of every lead and time sequence complexity data and using a full-new angle to continuously reflect time space characteristics of electroencephalic activation to observe the change process of human brain function state. Said invention can be used for making scalp electroencephalic dianosis in the reseach of epileptic focus location and induced response, also can be used for EEG signal analysis of brain being in different physiological conditions or senile dementia patient.

The invention discloses a program scoring system based on EEG (Electroencephalogram) emotion recognition, comprising an EEG signal acquisition module, an EEG signal pre-processing module, an EEG signal analysis module, an emotion recognition analysis module, a program effect analysis module and a program scoring module which are connected in order, wherein EEG signals of audiences are acquired bythe EEG signal acquisition module when the audiences are watching different slices and after completing watching for a period of time, then filtering operation is performed through the EEG signal pre-processing module, and feature extraction of the EEG signals is achieved through wavelet packet decomposition; after that, emotion recognition is performed on the extracted feature by the emotion recognition analysis module through a back propagation neural network; the program effect analysis module is used for monitoring duration of emotion influences of the audiences; and finally, scoring operation is performed on the corresponding program by the program scoring module. Through adoption of the program scoring system of the invention, actual influence of the program on the audiences can be analyzed and calculated from the EEG signals of the audiences, so as to perform scoring, therefore, outside interference can be eliminated, and the most actual effect of the program can be reflected.

The invention discloses a fatigue driving early warning system based on electroencephalogram analysis. The fatigue driving early warning system includes an electroencephalogram acquisition subsystem,an electroencephalogram analysis subsystem and a driving early warning subsystem, wherein the electroencephalogram acquisition subsystem used for acquiring electroencephalogram analog signals, obtaining and transmitting electroencephalogram digital signals by preprocessing; the electroencephalogram analysis subsystem used for extracting feature and analyzing of the electroencephalogram digital signals and conducting time domain waveform display, the electroencephalogram digital signals are recognized by using an SVM multiple-classification recognition algorithm, and the classification result of driving state dispersed grade which a driver is located is obtained and transmitted; the driving early warning subsystem includes an intelligent early warning hand ring used for conducting early warning control according to the driving state dispersed grade which the driver is located and a vehicle-mounted monitoring device used for conducting early warning control according to the driving statedispersed grade in which the driver is located. The fatigue driving early warning system uses the electroencephalogram signals to classify and recognize, whether the driver monitored by the vehicle-mounted monitoring device is in a heavy dispersed state or not judged, early warning is conducted, and the fatigue driving early warning system is high in judging precision, timely in early warning, convenient to carry and easy to operate and has the high practical value.

The invention discloses a brain-computer interface based telephone system comprising an electroencephalogram extractor, an electroencephalogram preprocessing unit and a data processing terminal. The electroencephalogram extractor is used for extracting electroencephalograms of a testee; the electroencephalogram preprocessing unit is connected with the electroencephalogram extractor; the data processing terminal is communicated with the electroencephalogram preprocessing unit. An electroencephalogram sampling unit comprises a first electroencephalogram electrode, a second electroencephalogram and a third electroencephalogram which sample occipital area level, frontal area level and aural area level respectively. The brain-computer interface based telephone system is simple in structure, small in size, reasonable in design, simple to operate and good in utilization effect; the demands of special populations for telephone usage can be met. The invention further discloses a brain-computer interface call method; the method includes the steps of 1, extracting and transmitting electroencephalograms, 2, sampling and synchronously uploading the electroencephalograms, and 3, analyzing the electroencephalograms. The call method is simple, convenient to implement and good in utilization effect; various telephone operations, such as dialing and answering, are automatically performed according to electroencephalograms of the testee.

The invention relates to a hybrid active rehabilitation method and device based on mirror neurons and a brain-computer interface. The invention aims to provide the hybrid active rehabilitation methodand device based on mirror neurons and the brain-computer interface, so as to improve the enthusiasm of rehabilitation training of a patient and promote reconstruction of brain functions. According tothe technical scheme, the hybrid active rehabilitation method based on the mirror neurons and the brain-computer interface is characterized in that the method comprises the steps: presenting the external stimulation capable of stimulating the mirror neurons of a patient to generate an active movement idea to the patient; acquiring an electroencephalogram signal containing an active motion idea ofthe patient; analyzing the motion intention of the patient according to the electroencephalogram signal; and controlling a motion execution module to assist the affected limb of the patient to complete motion corresponding to the patient motion intention according to the patient motion intention. The method is suitable for the field of brain rehabilitation training.

The invention discloses a data extraction method for epilepsy abnormal group activities in interval intracranial electroencephalogram signals. According to the method, distribution of epileptiform discharge events along channels and distribution of epileptiform discharge moments in short-time windows are obtained through an automatic detection algorithm of interval epileptiform discharge time, suspicious channels and abnormal time windows are selected according to the distribution, and preliminary screening signals of the epileptiform discharge events are obtained; the primary screening signals can improve the efficiency and quality of analyzing and interpreting the intracranial electroencephalogram signals by electrophysiology personnel; and group activity modes of the epileptiform discharge events are automatically extracted through a non-negative matrix factorization algorithm, the preliminary screening signals of the epileptiform discharge events are classified into different groupactivity modes, and the electrophysiology personnel are assisted in observing group discharge activity modes of multiple channels, so that the data size is reduced, and the utilization rate of the interval intracranial electroencephalogram signals is increased.

The invention discloses an elimination method for myoelectricity artifacts in small-number-channel brain electrical signals. The elimination method includes the steps that 1, the small-number-channel brain electrical signals are decomposed at the same time with multi-element experience empirical mode decomposition, and a small-number-channel intrinsic mode component matrix is obtained; 2, the small-number-channel intrinsic mode component matrix is subjected to blind signal separation through independent variable analysis; 3, components with the myoelectricity artifacts are judged with autocorrelation coefficients, myoelectricity artifact components are subjected to zero setting, and a component matrix without the myoelectricity artifacts is obtained through independent variable analysis inverse transformation; 4, according to the marshalling sequence of the original intrinsic mode component matrix, intrinsic mode components of corresponding channels are sequentially added, and clean brain electrical signals are finally obtained. By means of the elimination method, the influence of the myoelectricity artifacts on the brain electrical signals can be completely removed, and therefore the analysis accuracy of the brain electrical signals is improved.

The invention discloses a cabin environment intelligent control system based on electroencephalogram analysis. The cabin environment intelligent control system comprises an in-vehicle environment perception module, an in-vehicle air quality analysis module, an electroencephalograph monitoring module, an electroencephalogram analysis module, a control system module, and an execution system module.According to the cabin environment intelligent control system based on electroencephalogram analysis, cabin environmental parameters and electroencephalogram are coupled to be analyzed, analysis of human behavior information of a driver is integrated while the cabin environmental parameters are monitored, relevant parameters of the mental state and the degree of attention focusing of the current state of the driver are obtained, sources of environmental factors affecting the mental state of the driver are judged with the help of the environmental parameters, then through decision optimization,the optimal in-vehicle environment control scheme is obtained, thus executive components such as an in-vehicle air conditioner ventilation device, a purification device and a music lighting system work synergistically, the cabin environment can be adjusted in real time according to the driving experience of each driver, and the intelligent degree of air quality control inside a vehicle is improved.

The present invention discloses an unmanned aerial vehicle flight path planning system based on an electroencephalogram. The system comprises an interface module, a signal generation module, a data processing module, a brain machine interface module and an unmanned aerial vehicle flight path planning module. According to the technical solutions of the present invention, a navigation capability-based electroencephalogram can be measured, and an electrical signal that is generated for affecting a cognitive behavior of human in a navigation process is analyzed, so as to perfect an autonomous navigation capability of the unmanned aerial vehicle.

The invention provides a complexity spectrum electroencephalographic prediction and diagnosis method based on power spectrum division and belongs to the field of EEG signal analysis and brain metal disease prediction and diagnosis. The complexity spectrum electroencephalographic prediction and diagnosis method based on power spectrum division mainly includes an EEG signal complexity spectrum definition, analysis and extraction method based on power spectrum division and a nonlinear logistic complexity spectrum reference model construction method. First, a complexity spectrum based on power spectrum division is defined for EEG signals, the calculation method of the complexity spectrum is given, then, a data sequence generated through mapping is calculated through the complexity spectrum electroencephalographic prediction and diagnosis method, an analysis complexity spectrum reference model for the EEG signals is established on the basis, physical and biological meanings of the size, the number and the distribution of all structural spectral line sequences are analyzed, and a complexity spectrum reference space distribution model of the mapping based on power spectrum division is drawn. The complexity spectrum electroencephalographic prediction and diagnosis method based on power spectrum division can be used for predicting and analyzing brain metal diseases.

The invention discloses an electroencephalogram-based sleep quality analysis device and method. The problems that the user sleep quality is improved passively, the price is high, inconvenience is brought to use, and the user cannot know the sleep quality of himself / herself or improve the seep quality are solved. The device comprises an electroencephalogram analyzer and a user mobile phone client;the electroencephalogram analyzer comprises an electroencephalogram electrode assembly, an electroencephalogram processing module, an FPGA microcontroller, a Bluetooth module, a helmet and a power supply module; the electroencephalogram electrode assembly is installed on the helmet and connected with the electroencephalogram processing module through a wire, the electroencephalogram processing module is connected with the FPGA microcontroller through a wire, the FPGA microcontroller is connected with the Bluetooth module through an electric wire, the power supply module is connected with the electroencephalogram processing module, the FPGA microcontroller and the Bluetooth module through wires separately, and the electroencephalogram analyzer is connected with the user mobile phone clientthrough wireless communication. The invention further provides the electroencephalogram-based sleep quality analysis method.

The invention discloses a system and a method for detecting color perception based on electroencephalogram evoked potential. The system comprises a stimulation screen, an electroencephalogram extraction device, an electroencephalogram signal amplifier, an electroencephalogram signal collector, an electroencephalogram signal analysis module and a result output device. The stimulation screen is used for displaying a flash block with colors alternately changing at a certain frequency and applying visual stimulation to a testee. The electroencephalogram extraction device is used for extracting electroencephalogram signals of the testee after the testee receives the visual stimulation, and the extracted electroencephalogram signals are amplified by the electroencephalogram signal amplifier, transmitted to the electroencephalogram signal collector, collected, transmitted to the electroencephalogram signal analysis module, analyzed and then outputted by the result output device. According to the technical scheme, the color perception is objectively detected by the system and the method based on cross modulation components of the electroencephalogram evoked potential. The system is simple, convenient to operate, short in detection time and accurate in result.

The invention discloses a mathematics supernormal teenager brain functional connection network analysis method based on resting state EEG signals. The method comprises: based on a resting state EEG signal, using a phase lag algorithm based on Wilcoxon signed-rank test (PLWT) to establish a connection network, so as to measure the connection network using graph theory indexes, and depicting connection network characteristics of mathematics supernormal teenagers, including active brain regions and graph theory indexes. Compared with the prior art, the method has the following advantages: comparing the connection network characteristics of mathematics supernormal teenagers, depicted based on the resting state EEG signals, with that based on task state EEG signal analysis, obtained characteristics are simpler and more universal, and the applied PLWT method performs phase synchronization determination of EEG signals by a Wilcoxon signed-rank test framework, to explore internal connection network, prevent subjective setting of a threshold value, and reduce undetermined factors in the connection network analysis.

The invention discloses an autism electroencephalogram signal classification device based on a resting-state brain network, belongs to the technical field of biomedical information, and particularly relates to a pattern classification method in the field of brain-computer interfaces. According to the device, autism is recognized and diagnosed from the three aspects of information entropy, power spectrum and brain function network, the most common electroencephalogram signal analysis method at the current research stage is included, an SPN filter is innovatively used for diagnosing the autism patient, and the accuracy rate is close to 100%. Particularly, by researching resting-state brain networks of autism children and normal children, the network topology difference of the autism childrenand the normal children is explored, and comparison with other various methods is performed, so that the pathology of autism is explored to a certain extent, and a reliable basis is provided for clinical diagnosis of autism.

The invention discloses a signal analysis method and component of an electroencephalogram neural feedback system in combination with virtual reality. The method comprises the following steps of: acquiring an electroencephalogram signal of a resting state of a user, and setting a training initial threshold according to an electroencephalogram signal analysis result of the resting state; collectingelectroencephalogram signals generated in each training period and performing filtering processing, then calculating power spectral density by utilizing a pwelch function to obtain an average value ofthe power spectral density of the electroencephalogram signals in the corresponding frequency band, taking the average value as a feedback numerical value of the current training period, and adjusting a threshold value of the next training period according to the feedback numerical value; and calculating a feedback training learning effect index of the current training, and selecting an appropriate scene state by the virtual reality system according to the index for the next training. According to the method, the electroencephalogram signals generated in the training period of the user are processed in real time, the feedback numerical value is given, the threshold value is adjusted in real time according to the feedback numerical value, and the advantage that the task difficulty is dynamically adjusted according to the current cognitive ability of the user is achieved.