Abstract:

In order to preserve the spatial information between the electrodes and fully extract the characteristics of Electroencephalogram（EEG） and improve the accuracy of emotion recognition, a feature learning and classification algorithm based on convolutional neural network with 3D input is proposed. The single entropy（approximate entropy, permutation entropy and singular value decomposing entropy） and its combined entropy characteristics are used to perform emotion recognition experiments in the DEAP dataset on the two dimensions of valenceand arousal. The experimental results show that the accuracy of the combined entropy feature is significantly higher than that of the single entropy feature in the emotion recognition experiments. The average accuracy of the highest combined entropy characteristics are 94.14% and 94.44% in the valence and arousal, respectively, which are 5.05 percentage points and 4.49 percentage points higher than the average accuracy of the highest single entropy.

Key words: Electroencephalogram（EEG）, emotion recognition, approximate entropy, permutation entropy, singular value decomposition entropy, convolutional neural network, combined features

摘要：

为了保留电极之间的空间信息以及充分提取脑电信号（Electroencephalogram，EEG）特征，提高情感识别的准确率，提出一种基于三维输入卷积神经网络的特征学习和分类算法。采用单熵（近似熵（Approximate Entropy，ApEn）、排列熵（Permutation Entropy，PeEn）和奇异值分解熵（Singular value decomposition Entropy，SvdEn））以及其组合熵特征，分别在DEAP数据集进行效价和唤醒度两个维度上的情感识别实验。实验结果表明，采用组合熵特征比单熵特征在情感识别实验中准确率有显著提高。最高组合熵特征平均准确率在效价和唤醒度上分别为94.14%和94.44%，比最高单熵特征平均准确率分别提高了5.05个百分点和4.49个百分点。

关键词: 脑电信号, 情感识别, 近似熵, 排列熵, 奇异值分解熵, 卷积神经网络, 组合特征