Deep stack network-based electroencephalogram signal feature extraction and classification method

Abstract

The invention discloses a deep stack network-based electroencephalogram signal feature extraction and classification method. The method comprises the steps of firstly acquiring electroencephalogram signal data by using an Emotiv electroencephalogram signal acquisition instrument; performing preprocessing of mean removal, filtering, normalization and the like on electroencephalogram signals; then performing independent pre-training on electroencephalogram signals of a single channel by using a plurality of restricted Boltzmann machines, extracting the electroencephalogram signals of the single channel, and applying parameters obtained by training to parameter initialization of a neural network; finally performing micro-adjustment on the network by adopting a batch gradient descent method, and effectively fusing electroencephalogram signal features of all channels; and performing performance testing on the network and implementing classification. According to the method, relatively high classification accuracy can be obtained.

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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