基于时频空多维融合特征的脑电情感识别

EEG Emotion Recognition Based on Time-frequency-space Multi-dimensional Fusion Features

脑电信号(electroencephalogram,EEG)包含丰富的时间,空间和频率信息,是最能准确反映情感状态的生理信号,在情感识别领域发挥着重要作用。由于单特征的脑电情感识别研究方法存在缺失信息的问题,因此提出了三维融合特征的脑电信息处理方法,将脑电信号的微分熵频域特征和8种时域特征进行融合,并按照电极片位置信息进行空间排布,构建脑电信号的三维融合特征。将注意力机制引入多任务卷积神经网络(multi task attention convolutional neural network,MTA-CNN),并将构造的三维特征作为输入进行测试分析。结果表明,所提出模型方法在DEAP数据集的效价维和唤醒维二分类问题准确率均有显著提升。

Electroencephalogram(EEG)contains a wealth of temporal,spatial and frequency information,which is the most accurate physiological signal to reflect the emotional state,and plays an important role in the field of emotion recognition.Because the single feature EEG emotion recognition method has the problem of missing information,the EEG information processing method of three-dimensional fusion features was proposed.The EEG signal differential entropy frequency domain features and eight kinds of time domain features were fused,and according to the electrode position information for spatial arrangement,the EEG signal three-dimensional fusion feature was constructed.The attention mechanism was introduced into the multi-task convolutional neural network(MTA-CNN),and the constructed three-dimensional features were used as the input for test and analysis.The results show that the proposed model and method can significantly improve the classification accuracy in the valence dimension and arousal dimension of DEAP data set.