关键词: 脑电信号, 卷积神经网络, 深度学习, 情感识别, 组合特征

Abstract: In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram（EEG）, an EEG emotional feature learning and classification method using deep Convolution Neural Network（CNN） models is proposed based on temporal features, frequential features and their combination features of EEG signals in DEAP dataset. The shallow machine learning models including Bagging Tree（BT）, Support Vector Machine（SVM）, Linear Discriminant Analysis（LDA） and Bayesian Linear Discriminant Analysis（BLDA） models and deep CNN models are used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results show that the deep CNN models achieve the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

Key words: electroencephalogram（EEG）, Convolution Neural Network（CNN）, deep learning, emotion recognition, combined features