Emotion electroencephalogram signal classification method based on cross-connection type convolutional neural network

Abstract

The invention provides a bridged convolutional neural network-based emotion electroencephalogram signal classification method, which comprises the following steps of: firstly, extracting electroencephalogram signal bottom-layer features by using a first convolutional layer of V3, taking the electroencephalogram signal bottom-layer features as the input of V1, and inputting the V1 into a third convolutional layer to extract middle-layer features after the V1 is down-sampled by a second pooling layer; and the middle-layer feature is used as the input of V2, is down-sampled by the fourth poolinglayer of V3 and then is input to the fifth convolution layer of V3 to extract the high-layer feature. And then, the three layers of features are respectively subjected to dimensionality reduction andthen are input into an eighth full-connection layer of the V3 for fusion, and finally, the three layers of features enter a Softmax layer for classification. And comparing the classification result with the actual label, calculating a loss value, and then updating the convolution kernel and the connection weight by using a back propagation algorithm. According to the method, the electroencephalogram signal classification accuracy can be high, and the recognition result is superior to that of a traditional machine learning method and a traditional CNN model.

Description

technical field [0001] The invention belongs to the field of deep learning, and relates to a deep learning classification method based on EEG, in particular to a deep learning classification method for emotional EEG signals. Background technique [0002] Emotion recognition is a research direction that is receiving increasing attention in the field of artificial intelligence. It mainly includes facial expression, voice, physiological pattern, text and physiological signal recognition, among which electroencephalogram (EEG) is the most informative physiological signal. , has received increasing research attention. When we use traditional feature-based machine learning classification methods, the effect of classification will depend on the quality of the selected features. However, due to the characteristics of non-stationarity and large inter-individual differences in the EEG signal itself, it is difficult to find a unified representative feature, which makes the accuracy of...

AI Technical Summary

Problems solved by technology

At present, there are few studies on the use of deep learning for EEG signal recognition at home and abroad, and there is a lot of research space

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