A Deep Learning Method with Self-Attention Mechanism for Cross-Subject Sleep Stage Classification Based on EEG and EOG

Jianjun Huang, Jun Qu
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Abstract

Brain-computer interface (BCI) systems based on electroencephalography (EEG) and electrooculogram (EOG) were shown to be able to be used for automatic sleep stage classification since these two signals contain many sleep characteristics. However, EEG signal characteristics vary greatly among individuals, and manual classification is time-consuming and subjective. This paper proposes a deep learning method using a self-attention mechanism to achieve cross-subject sleep stage classification based on EEG and EOG. The method mainly consists of three parts. First, a traditional convolutional neural network is used to perform preliminary feature extraction on the information of the two channels. Then use Long Short-Term Memory (LSTM) to find the features in time series. Finally, the self-attention mechanism is used to find more mission-critical information from the high-dimensional feature information. We performed 25-fold cross-validation experiments and showed that the model achieved an average accuracy of 82.4% and 80.4 of the macro-averaging F1 Score(MF1).
基于脑电和眼电的跨主体睡眠阶段分类的自注意深度学习方法
基于脑电图(EEG)和眼电图(EOG)的脑机接口(BCI)系统可以用于自动睡眠阶段分类,因为这两种信号包含许多睡眠特征。然而,脑电图信号的特征在个体之间差异很大,人工分类费时且主观。本文提出了一种利用自注意机制的深度学习方法,实现基于EEG和EOG的跨主体睡眠阶段分类。该方法主要由三部分组成。首先,利用传统的卷积神经网络对两个通道的信息进行初步的特征提取;然后利用长短期记忆法(LSTM)在时间序列中寻找特征。最后,利用自注意机制从高维特征信息中发现更多的关键任务信息。我们进行了25次交叉验证实验,结果表明,该模型的平均准确率为82.4%,达到宏观平均F1分数(MF1)的80.4。
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