A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification
A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification
复制标题
一种关注基于脑电图的情绪分类的分层双向 GRU 模型
DOI：
10.1109/access.2019.2936817
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhang, N.
中科院分区：
文献类型：
--
作者：
Chen, J. X.;Jiang, D. M.;Zhang, N.
在本文中，我们提出了一种分层双向门控循环单元（GRU）网络，专注于从连续脑电信号（EEG）信号中进行人类情感分类。该模型的结构反映了脑电信号的层次结构，并在脑电信号样本和时期两个层次上使用了注意机制。通过对不同重要性的内容给予不同程度的关注，该模型可以学习到更显著的脑电序列特征表示，突出重要样本和时期对其情感类别的贡献。我们在DEAP数据集上进行了跨学科的情感分类实验，以评估模型的性能。实验结果表明，在效价和唤醒维度上，我们的1-s分段EEG序列模型比最好的深基线LSTM模型分别高出4.2%和4.6%，比最好的浅基线模型分别高出11.7%和12%。此外，随着脑电序列历元长度的增加，该模型表现出比基线模型更强的鲁棒性，表明该模型能够有效降低脑电序列长期非平稳性的影响，提高基于脑电的情感分类的准确性和鲁棒性。
In this paper, we propose a hierarchical bidirectional Gated Recurrent Unit (GRU) network with attention for human emotion classification from continues electroencephalogram (EEG) signals. The structure of the model mirrors the hierarchical structure of EEG signals, and the attention mechanism is used at two levels of EEG samples and epochs. By paying different levels of attention to content with different importance, the model can learn more significant feature representation of EEG sequence which highlights the contribution of important samples and epochs to its emotional categories. We conduct the cross-subject emotion classification experiments on DEAP data set to evaluate the model performance. The experimental results show that in valence and arousal dimensions, our model on 1-s segmented EEG sequences outperforms the best deep baseline LSTM model by 4.2% and 4.6%, and outperforms the best shallow baseline model by 11.7% and 12% respectively. Moreover, with increase of the epoch’s length of EEG sequences, our model shows more robust classification performance than baseline models, which demonstrates that the proposed model can effectively reduce the impact of long-term non-stationarity of EEG sequences and improve the accuracy and robustness of EEG-based emotion classification.