Emotion recognition using electroencephalogram (EEG) signals had attracted significant research attention. This paper introduced a new approach, Multi-scale-res BiLSTM (MRBiL), to enhance EEG emotion recognition. Firstly, differential entropy features were extracted from EEG data during both resting and active states. The disparity between these two states was then calculated to generate a feature matrix, which was subsequently input into a multi-scale convolution block. The high-dimensional feature matrix extracted from the convolution block was mapped using a residual block. The feature signal sequence was then processed by a bidirectional long-term short-term memory network. Finally, the emotion recognition result was obtained through the classification layer. The algorithm was validated in the DEAP dataset, resulting in average accuracies of 0.9788 for binary classification of validity and arousal. Furthermore, the algorithm attained an average accuracy of 0.9685 for quadruple classification. Additionally, ablation experiments were conducted in this study to affirm the effectiveness of each deep learning component within MRBiL. The experimental results demonstrated that the novel neural network model proposed in this paper had outperformed currently available methods in emotion recognition tasks.

使用脑电图 （EEG） 信号的情绪识别引起了研究的极大关注。本文介绍了一种新方法，多尺度分辨率 BiLSTM （MRBiL），以增强脑电图情绪识别。首先，从静息和活动状态下的 EEG 数据中提取差分熵特征;然后计算这两种状态之间的差异以生成一个特征矩阵，然后将其输入到多尺度卷积块中。从卷积块中提取的高维特征矩阵使用残差块进行映射。然后，特征信号序列由双向长时短时记忆网络处理。最后，通过分类层得到情感识别结果。该算法在 DEAP 数据集中进行了验证，有效性和唤醒度的二元分类的平均准确率为 0.9788。此外，该算法的四重分类平均准确率为 0.9685。此外，本研究还进行了消融实验，以确认 MRBiL 中每个深度学习组件的有效性。实验结果表明，本文提出的新型神经网络模型在情感识别任务中优于目前可用的方法。