多通道连续卷积神经网络脑电信号情绪识别

针对不同个体的脑电信号差异大且易受到环境因素影响的问题, 结合去基线干扰及脑电通道选择方法, 提出一种基于连续卷积神经网络的情绪分类识别算法. 首先进行基线信号的微分熵(differential entropy, DE)特征的选取研究, 将数据处理为多通道输入后使用连续卷积神经网络进行分类实验, 然后选择最佳电极个数. 实验结果表明, 将实验脑电信号微分熵与被试者实验脑电前一秒的基线信号微分熵的差值映射为二维矩阵后, 在频率维度组合为多通道的形式作为连续卷积神经网络的输入, 在22通道上唤醒度和效价的分类平均准确率为95.63%和95.13%, 接近32通道的平均准确率.

Emotion Recognition of EEG Signals Based on Multi-channel and Continuous CNN

Individual electroencephalogram (EEG) signals are different and vulnerable to environmental factors. In view of these problems, this study adopts methods of removing baseline interference and EEG channel selection and proposes an emotion classification and recognition algorithm based on a continuous convolutional neural network (CNN). Firstly, the selection of differential entropy (DE) characteristics of baseline signals is studied. After the data is processed into multi-channel input, the continuous CNN is used for classification experiments, and then the optimal number of electrodes is determined. The experimental results show that after the difference between the DE of experimental EEG signals and that of baseline signals of the subject one second before the experimental EEG is mapped into a two-dimensional matrix, and the frequency dimension is turned into a multi-channel form to serve as the input of the continuous CNN, the average classification accuracy of arousal and valence on 22 channel is 95.63% and 95.13%, respectively, which are close to that on 32 channel.