A portable and lightweight human brain state detection method based on data fusion

Abstract

The invention discloses a portable and lightweight human brain state detection method based on data fusion. The preprocessed EEG signal data is subjected to blind source signal separation to obtain signals from multiple signal sources. Based on the wavelet packet transform, feature extraction is performed on the signals of each signal source; each signal source is input to multiple trained Analysis is performed in the lightweight convolutional neural network model, and weighted voting is performed on the outputs of multiple lightweight convolutional neural network models to obtain the final classification result; the lightweight convolutional neural network model is composed of wavelet packets for each signal source. The transformed features are used as input, and the signal source category is used as output.

Description

technical field [0001] The invention belongs to the technical field of brain-computer interface, and in particular relates to a portable lightweight human brain state detection method based on data fusion. Background technique [0002] At present, people's demand for physiological state monitoring is increasing day by day. It is very important to use EEG signals to monitor people's physiological state. In the traditional monitoring based on EEG signals, the characteristics of the signal are first extracted by time-frequency analysis method, and then the signal is analyzed by machine learning methods such as SVM and k-means. However, the final analysis accuracy of these methods is not ideal. With the emergence of deep learning, CNN, RNN and other methods have also performed well in the analysis of EEG signals. However, due to the high structural risk of the deep learning model, the model is prone to poor generalization ability, over-fitting, and poor real-time performance....

AI Technical Summary

Problems solved by technology

[0003] The technical problems to be solved by the present invention are: the low accuracy of EEG signal analysis based on a small number of channels and the poor training time and real-time response time of the analysis model based on deep learning, as well as the fact that the analysis of EEG signals in the past used many The problem of inconvenient portability

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