Emotion Classification Using EEG Data in a Brain-Inspired Spiking Neural Network

Abstract

As an important application of emotion artificial intelligence, emotion classification provides the basis for the realization of affective brain-computer interface (aBCI). In this study, the NeuCube is used to learn and classify Electroencephalogram (EEG) data from the DEAP dataset. NeuCube is a type of spiking neural network (SNN) framework developed based on the real human brain. It is very suitable for analyzing and processing spatio-temporal data. Based on the 10-fold cross-validation method, we obtain a mean accuracy of 68.91 % in the emotional binary valence classification problem. Meanwhile, the EEG data recorded from F3 and F4 electrode channels provide more information compared with Fp1 and Fp2. The results prove that the spiking neural network can be applied to the task of emotion classification effectively.