In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still a challenging task to design a calibration-free system, since there exists a significant variability of EEG signals among different subjects and recording sessions. As deep learning has received much research attention in recent years, many efforts have been made to use deep learning methods for EEG signal recognition. However, existing works mostly treat deep learning models as blackbox classifiers, while what have been learned by the models and to which extent they are affected by the noise from EEG data are still underexplored. In this paper, we develop a novel convolutional neural network that can explain its decision by highlighting the local areas of the input sample that contain important information for the classification. The network has a compact structure for ease of interpretation and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.4%-72.68% and state-of-art deep learning methods of 63.90%-65.61%. Visualization results show that the model has learned to recognize biologically explainable features from EEG signals, e.g., Alpha spindles, as strong indicators of drowsiness across different subjects. In addition, we also explore reasons behind some wrongly classified samples and how the model is affected by artifacts and noise in the data. Our work illustrates a promising direction on using interpretable deep learning models to discover meaning patterns related to different mental states from complex EEG signals.

中文翻译：

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基于 EEG 的跨学科驾驶员睡意识别与可解释的 CNN

在基于脑电图 (EEG) 的驾驶员睡意识别的背景下，设计免校准系统仍然是一项具有挑战性的任务，因为不同受试者和记录会话之间的 EEG 信号存在显着差异。由于近年来深度学习受到了很多研究的关注，人们已经做出了许多努力来使用深度学习方法进行脑电信号识别。然而，现有的工作大多将深度学习模型视为黑盒分类器，而模型已学习到的内容以及它们在多大程度上受 EEG 数据噪声的影响仍未得到充分探索。在本文中，我们开发了一种新颖的卷积神经网络，它可以通过突出显示包含分类重要信息的输入样本的局部区域来解释其决策。该网络具有易于解释的紧凑结构，并利用可分离卷积以时空序列处理 EEG 信号。结果表明，该模型在 11 个科目上实现了 78.35% 的留一法跨科目睡意识别的平均准确率，高于传统的 53.4%-72.68% 的基线方法和最先进的深度学习方法63.90%-65.61%。可视化结果表明，该模型已经学会了从 EEG 信号（例如 Alpha 纺锤波）中识别出生物学上可解释的特征，作为不同受试者嗜睡的有力指标。此外，我们还探讨了一些错误分类样本背后的原因，以及模型如何受到数据中的伪影和噪声的影响。