Cross-subject EEG fatigue state classification method based on generative adversarial domain self-adaptation

Abstract

The invention discloses a cross-subject EEG fatigue state classification method based on generative adversarial domain self-adaptation. The method comprises the following steps: firstly, acquiring andpre-treating data, and removing artifacts; secondly, performing EEG feature extraction through PSD, and acquiring a two-dimensional sample matrix from a three-dimensional EEG time sequence; distinguishing a source domain data set and a target domain data set to obtain a training set and a test set which are not overlapped; training a classification model GDANN by using part of label-free target domain data and random data conforming to Gaussian distribution; and finally, evaluating the accuracy of the classification result by using a confusion matrix. The generative adversarial network and the domain invariant thought are further combined, the problem that EEG signal data sets are rare and difficult to obtain is solved, the problem that source domain data and target domain data are not matched is balanced, negative migration is avoided to a certain extent, a high-precision cross-subject fatigue detection classifier is trained, and the method is expected to have a wide application prospect in actual brain-computer interaction.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal (EEG) fatigue state recognition in the field of biological feature recognition, and in particular relates to a cross-subject EEG fatigue state classification method based on generational confrontation domain self-adaptation. Background technique [0002] Mental fatigue is a complex physical and psychological state that can lead to decreased alertness, concentration and cognitive abilities. About 1.3 million people die in traffic accidents every year in the world, and fatigue driving is the main factor therein. Therefore, how to effectively detect and predict the mental state while driving is very important for reducing the loss of life and property caused by fatigue driving. [0003] In recent years, many fatigue driving detection methods have been proposed. Among them, the mental state analysis method based on EEG is an effective and objective fatigue detection method. Because the EE...

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Problems solved by technology

[0006] In order to overcome the problems of poor discriminative performance in cross-subject EEG fatigue state classification of the above-mentioned prior art, data mismatch in source domain and target domain, and negative transfer caused by too many source domain samples, the present invention proposes a method based on generative confrontational domain adaptive Cross-subject EEG fatigue state classification method Generative-DANN (GDANN)

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