A method for feature extraction and classification of EEG signals based on deep stacked network

Abstract

The invention discloses a method for extracting and classifying EEG signal features based on a deep layer stacking network. First, Emotiv EEG signal acquisition instrument is used to collect EEG signal data; the EEG signal is subjected to preprocessing such as de-meaning, filtering, and normalization. ; Then use multiple restricted Boltzmann machines to independently pre-train the single-channel EEG signal, extract the single-channel EEG signal, and use the trained parameters for the parameter initialization of the neural network; finally, use batch gradient descent The method is used to fine-tune the network and effectively integrate the EEG signal features of each channel; to test the performance of the network and realize classification. The present invention can obtain higher classification accuracy.

Description

technical field [0001] The invention relates to the technical field of feature extraction and classification methods of EEG signals, in particular to a method for feature extraction and classification of EEG signals based on a deep stack network. Background technique [0002] Brain-computer interface (BCI) is a human-computer interaction method that directly communicates with computers or external devices through the human brain. BCI technology provides a new information exchange channel for paralyzed patients, can improve the quality of life of patients, and has great practical value in the medical field, cognitive science, psychology, military field, entertainment and wearable smart equipment fields. [0003] The recognition of electroencephalogram signal (EEG) is the key technology of BCI, including signal preprocessing, feature extraction and feature classification. Commonly used EEG signal feature extraction methods include autoregressive (AR) model, wavelet transform,...

AI Technical Summary

Problems solved by technology

[0004] Traditional supervised learning needs to collect a large amount of labeled EEG data to train the classifier. Obtaining a large number of labeled samples not only requires a lot of human and material resources, but also may eliminate some hidden useful information during data processing. , so the features extracted by the traditional feature extraction method are not enough for a good analysis of the recognition process of the EEG signal

Although unsupervised learning uses unlabeled EEG data to train classifiers, due to the lack of information about labeled EEG data, it is easy to lead to a decline in the generalization ability of the model, resulting in low classification accuracy.

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