In brain-computer-interface (BCI) devices, signal acquisition via reducing the electrode channels can reduce the computational complexity of models and filter out the irrelevant noise. Differential entropy (DE) plays an important role in emotional components of signals, which can reflect the area activity differences. Therefore, to extract distinctive feature signals and improve the recognition accuracy based on feature signals, a method of DE feature signal recognition based on a Convolutional Gated Recurrent Unit network was proposed in this paper. Firstly, the DE and power spectral density (PSD) of each original signal were mapped to two topographic maps, and the activated channels could be selected in activation modes. Secondly, according to the position of original electrodes, 1D feature signal sequences with four bands were reconstructed into a 3D feature signal matrix, and a radial basis function interpolation was used to fill in zero values. Then, the 3D feature signal matrices were fed into a 2D Convolutional Neural Network (2DCNN) for spatial feature extraction, and the 1D feature signal sequences were fed into a bidirectional Gated Recurrent Unit (BiGRU) network for temporal feature extraction. Finally, the spatial-temporal features were fused by a fully connected layer, and recognition experiments based on DE feature signals at the different time scales were carried out on a DEAP dataset. The experimental results showed that there were different activation modes at different time scales, and the reduction of the electrode channel could achieve a similar accuracy with all channels. The proposed method achieved 87.89% on arousal and 88.69% on valence.

在脑机接口（BCI）设备中，通过减少电极通道来获取信号可以降低模型的计算复杂度并滤除不相关的噪声。微分熵（DE）在信号的情感成分中起着重要作用，可以反映区域活动的差异。因此，为了提取特征信号并提高基于特征信号的识别精度，提出了一种基于卷积门控递归单元网络的DE特征信号识别方法。首先，将每个原始信号的DE和功率谱密度（PSD）映射到两个地形图，然后可以在激活模式下选择激活的通道。其次，根据原始电极的位置，将具有四个频带的一维特征信号序列重建为3D特征信号矩阵，并使用径向基函数插值填充零值。然后，将3D特征信号矩阵馈入2D卷积神经网络（2DCNN）以进行空间特征提取，并将1D特征信号序列馈入双向门控递归单元（BiGRU）网络以进行时域特征提取。最后，将时空特征通过一个全连接层融合，并在DEAP数据集上进行基于DE信号在不同时间尺度的识别实验。实验结果表明，在不同的时间尺度上存在不同的激活模式，电极通道的减少可以在所有通道上达到相似的精度。所提出的方法达到了87。