关键词: 脑电信号（EEG）, 特征提取, 卷积神经网络（CNN）, 随机森林, 损失函数

Abstract: Considering that traditional machine learning requires artificial construction features and low feature quality, this paper proposes a novel automatic feature extraction approach in Electroencephalograph（EEG） signals based on 1-D Convolutional Neural Network（CNN）. This approach uses the idea of compilation, at the same time the convolutional layer and the downsampling layer form the encoder network to extract the emotional characteristics of the EEG signal, then the Leaky ReLU activation function is applied to the feature map. For the convolution pre-training process, the cross-entropy and regularization terms are used to optimize the loss function, then the random forest classifier is used to obtain the emotion classification label. Finally, the experiment is carried out on the international public data set SEED, which achieves 94.7% sentiment classification accuracy, and the experimental results show the effectiveness and robustness of the proposed method.

Key words: Electroencephalograph（EEG）, feature extracting, Convolutional Neural Network（CNN）, fandom forest, loss function