P300 feature extraction method based on wavelet transformation and Fisher criterion

Abstract

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.

Description

technical field [0001] The invention belongs to the combined application of the field of cognitive neuroscience and the field of information technology, and relates to a P300 feature extraction method of an event-related potential, specifically a P300 feature extraction method based on wavelet transform and Fisher criterion. Background technique [0002] The brain-computer interface is a new way for patients with motor function loss but intact brain function to communicate with the outside world. P300 Speller is a way of brain-computer interface. Its function is to analyze the user's EEG signal and identify the characters it wants to output, so as to help users communicate with the outside world. The feature extraction method currently used in the P300 Speller is: down-sampling the EEG data segments extracted from each channel, and the obtained ones are used as features. This feature extraction method has the problem of unclear features, which affects the effect of the clas...

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Problems solved by technology

[0004] In order to solve the defects of unclear feature extraction and low practicability of the EEG signal in the prior art, the present invention provides a P300 feature extraction method based on the combination of wavelet transform and Fisher criterion, which can make the classification The effect of the device is improved, and on the premise of meeting the accuracy requirements, it can achieve the purpose of reducing the number of stimulation repetitions and increasing the character transmission rate

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