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Abstract

The present disclosure discloses an electroencephalogram (EEG) emotion recognition method based on a spiking convolutional neural network. In the present disclosure, after EEG emotion data is preprocessed, a differential entropy feature is extracted and a time window is intercepted, and then a spiking convolutional encoder is configured to encode the EEG emotion data; a spiking convolutional feature extractor extracts a spiking data feature; and a spiking fully connected classifier performs learning and classification. In the present disclosure, a spiking neural network combining a spiking convolutional layer and a spiking fully connected layer is trained directly to achieve an objective of EEG emotion classification. In a training process, spatio-temporal information in EEG data is extracted by transferring a spiking between layers of the network and between time slices by using the spiking convolutional layer, and then an emotion classification task is performed through feature learning of the spiking fully connected layer.

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