EEG Emotion Recognition Based on Multi-channel Input and Multi-scale Convolutional Neural Network
Aiming at the problem that the electroencephalogram(EEG)features extracted by traditional methods are single and insufficient,this paper improves the classical convolution neural network,proposes a convolution neural network(CNN)model with different scale convolution kernel,and verifies it on DEAP dataset.In this experiment,the differential entropy(DE)feature is extracted by combining the baseline signal of each subject.The preprocessed results are combined into four channel form in the fre-quency dimension.Finally,the classification experiment is input into the CNN model with different scale convolution kernel.The classification accuracies of this method in arousal and valence are 97.77%and 97.55%.Compared with the single scale CNN model,the accuracies are significantly improved,which proves the effectiveness of the network model in this paper.
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