针对现有的情感识别方法在时间上不能充分利用信息的问题,频率,和EEG信号中的空间域,这导致脑电情绪分类的准确性较低,本文提出了一种多特征,基于多频带的跨尺度注意卷积模型(CATM)。该模型主要由跨尺度的注意力模块,频率空间注意模块,一个功能转换模块,时间特征提取模块,和深度分类模块。首先,跨尺度注意卷积模块对预处理后的脑电信号提取不同尺度的空间特征;然后,频率空间注意模块为重要通道和空间位置分配更高的权重;接下来,时间特征提取模块提取脑电信号的时间特征;最后,深度分类模块将EEG信号分类为情绪。我们在DEAP数据集上评估了所提出的方法,在效价和唤醒二元分类实验中的准确率分别为99.70%和99.74%,效价-唤醒四分类实验的准确率分别为97.27%。此外,考虑到较少渠道的应用,我们还进行了5通道实验,效价和唤醒的二元分类准确率分别为97.96%和98.11%,分别。效价-唤醒四分类准确率为92.86%。实验结果表明,与其他最新方法相比,本文提出的方法具有更好的效果,并且在少数通道实验中也取得了更好的结果。
Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency-space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency-space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and
arousal binary classification experiments, respectively; the accuracy in the valence-
arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and
arousal were 97.96% and 98.11%, respectively. The valence-
arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.