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

摘要： 为了点对点自动学习脑电信号（Electroencephalogram，EEG）空间与时间维度上的情感相关特征，提高脑电信号情感识别的准确率，基于DEAP数据集中EEG信号的时域、频域特征及其组合特征，提出一种基于卷积神经网络（Convolution Neural Network，CNN）模型的EEG情感特征学习与分类算法。采用包括集成决策树、支持向量机、线性判别分析和贝叶斯线性判别分析算法在内的浅层机器学习模型与CNN深度学习模型对DEAP数据集进行效价和唤醒度两个维度上的情感分类实验。实验结果表明，在效价和唤醒度两个维度上，深度CNN模型在时域和频域组合特征上均取得了目前最好的两类识别性能，在效价维度上比最佳的传统分类器集成决策树模型提高了3.58%，在唤醒度上比集成决策树模型的最好性能提高了3.29%。

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