The invention discloses a motor imagery electroencephalogram classification method based on space-time-frequency optimization feature sparse representation. Lead, time period and frequency band which are most conductive to classification are selected mainly based on a linear discriminant criterion, electroencephalogram features are extracted through a common spatial pattern algorithm, and finally, classification is carried out according to feature sparse representation. The method comprises the steps of electroencephalogram signal preprocessing, lead selection, time-frequency block selection, feature extraction, and feature classification. Results show that lead, time period and frequency band which are most conductive to classification can be selected, and a good classification result can be obtained through sparse representation of features extracted using the common spatial pattern algorithm. Compared with the existing algorithm, space-time-frequency parameters most conductive to classification can be selected automatically, the features in an optimal time-frequency block can be combined, and the accuracy of motor imagery electroencephalogram signal classification can be improved.

Motor imagery electroencephalogram classification method based on space-time-frequency optimization feature sparse representation

The invention relates to a multivariable causal relationship analyzing method based on lag order adaptive selection, and aims to accurately forecast the causal effect between the brain areas based on a motion imagination electroencephalogram signal. The current traditional Granger causal relationship method based on an autoregressive model has the defects that a time-lag depend structure existing in a multivariable time sequence and the effect of a model coefficient to the causality cannot be adequately considered. The method comprises the following steps: to obtain a multi-channel motion imagination electroencephalogram signal firstly, using an improved backward time selection algorithm to estimate the optimal lag order of each variable in the autoregressive model, building a dynamic autoregressive model of the multi-channel electroencephalogram signal, and using the residual error and coefficient of the model to define the condition causal measure between multiple variables, effectively improving the estimation capacity of the real causal relationship. The method has the extensive application prospect in the causal brain function network and cortex muscle coupling analysis fields.

Multivariable causal relationship analyzing method based on lag order adaptive selection

The invention discloses a method for EEG identity recognition based on dimension reduction with a genetic algorithm. The method is used to conduct identity recognition according to a user's EEG signals and specifically comprises the steps that EEG data is collected; the EEG data is pre-processed; EEG characteristic parameters are extracted by an AR model; a classifier of a BP neural network is established; the genetic algorithm is used for dimension reduction of the EEG parameters; and a classifier is established according to the dimension-reduced EEG parameters and used for the identity recognition. The method uses the genetic algorithm to conduct the dimension reduction of the EEG parameters creatively; the method can effectively reduce data dimensions and reserve information beneficial for EEG recognition at the same time, so a higher recognition rate can be maintained; and the method is also applicable to EEG data of an individual which is not recognized before, namely the method can still keep a very high recognition rate in set opening verification.

Method for EEG identity recognition based on dimension reduction with genetic algorithm

The invention relates to a brain-eye hybrid driven household electrical appliance control method. The brain-eye hybrid driven household electrical appliance control method allows the user to transmit a control command to the system only depending on ocular fixation and movement of eyes and electroencephalogram under the influence of attention. The system implementing the method has three states: the starting state, the electric appliance selection state and the keyboard selection state. In different states of the system, the user can express the self intention by concentrating the attention to look at the corresponding object. The signals related with system detection can decide switching of the system among the three states, and at the same time, switching of the related operation complete state, such as standby and rollback, of the system can be reasonably set. The brain-eye hybrid driven household electrical appliance control method can directly use the brain and eyes to realize control of household electrical appliance equipment.

Brain-eye hybrid driven household electrical appliance control method

The invention provides a single-time P300 detection method based on matrix grey modeling, belongs to the field of cognitive neuroscience and relates to a feature extraction and recognition detection method of an event related potential P300, in particular to a single-time P300 detection method based on matrix grey modeling in a grey theory.The method comprises the steps that 1, a original acquired electroencephalogram signal is preprocessed; 2, electrocardiographic lead combinations are selected, namely four electrocardiographic leads with most obvious top occipital region waveform differences are selected as optimal electrodes according to oscillograms of target stimulus and non-target stimulus of training set data; 3, segmented matrix grey modeling is conducted on the data of the four electrocardiographic leads, and model parameters are extracted to serve as feature vectors; 4, a Fisher ratio value method is utilized to perform optimal feature selection, and meanwhile the purpose of decreasing the number of feature vector dimensions is achieved; 5, a support vector machine classifier is utilized to classify feature vectors, and single-time P300 detection recognition is achieved.Experimental data tests show that the method can improve single-time P300 detection recognition rate, and the correct recognition rate can be further improved during less-time superposition.

Single-time P300 detection method based on matrix grey modeling

The invention relates to an AR coefficient space based ELM (extreme learning machine) motor imagination electroencephalogram classification method. The method includes: taking a single-channel motor imagination electroencephalogram signal as a random signal, and adopting an order-p AR model for fitting; solving to-be-determined coefficients a1, a2,... , ap of the AR model according to a Burg algorithm; combining order-p AR coefficients of m-channel motor imagination electroencephalogram signal into an AR coefficient vector, inserting into an original optimization problem of ELM network parameter training to solve an optimal outer weight beta, and establishing an AR coefficient space based ELM classification algorithm. By the AR coefficient space based ELM motor imagination electroencephalogram classification method, accuracy in classification of the motor imagination electroencephalogram signals is improved while classification speed of the motor imagination electroencephalogram signals is increased.

AR coefficient space based ELM (extreme learning machine) motor imagination electroencephalogram classification method

The present invention discloses a brain-computer interface control method for simulating a keyboard and a mouse. The method allows a user to transmit a control command to a controlled device by means of only cognitive activities in the brain in a manner similar to comprehensive use of a keyboard and a mouse. The manner is highly versatile. A brain-computer interface system that implements the method has three states: a resting state, a mouse state, and a keyboard state. A user expresses an intention by staring at blinking in different areas on a screen of the system or by performing motor imagery; the brain-computer interface system detects whether SSVEP, P300, or an event related desynchronization phenomenon corresponding to the motor imagery exists in the electroencephalogram of the user, so as to determine switching of the brain-computer interface system between the three states; the brain-computer interface system in the resting state does not receive user input, the brain-computer interface system in the mouse state allows the user to transmit a control command to a controlled device by using a virtual mouse, and the brain-computer interface system in the keyboard state allows the user to transmit a control command to a controlled device by using a virtual keyboard.

Brain-computer interface control method for simulating keyboard and mouse

Based on combination between wavelet transform and a Fisher criterion, the invention provides an EEG feature extraction method specific to a P300 Speller Brain-machine interface, mainly comprising the steps of: configuring a wavelet transform matrix for an EEG data segment of a specific length according to a wavelet function and a wavelet decomposition level designated by a user; utilizing a wavelet transform matrix to map the EEG data segment to a wavelet domain, and utilizing the Fisher criterion to search for a projection axis which can distinguish different types of data to a maximum degree in the wavelet domain; utilizing the projection axis to extract a plurality of rows from the wavelet transform matrix to form a feature extraction matrix; mapping a corresponding EEG data segment to a feature vector by a feature extraction matrix of each channel; and splicing feature vectors on all channels to form a feature vector for each time of stimulation. The method stimulates a corresponding EEG data segment each time to calculate a feature vector, and can reduce times of stimulation repetition under the condition of meeting accuracy rate requirements so as to increases a character transfer rate.

P300 feature extraction method based on wavelet transformation and Fisher criterion

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Papers

AR coefficient space based ELM (extreme learning machine) motor imagination electroencephalogram classification method

Brain-computer interface control method for simulating keyboard and mouse

P300 feature extraction method based on wavelet transformation and Fisher criterion