Deep neural networks (DNN) have become increasingly utilized in brain-computer interface (BCI) technologies with the outset goal of classifying human physiological signals in computer-readable format. While our present understanding of DNN usage for BCI is promising, we have little experience in deciphering neural events from dynamic freely-mobile situations. Using an improved version of EEGNet, our goal was to classify cognitive events from electroencephalography (EEG) signals while subjects simultaneously walked on a treadmill, sometimes while carrying a rucksack equivalent to 40% of their body weight. Walking subjects simultaneously performed a visual oddball target detection task, eliciting the P300 event-related potential (ERP), which then served as the DNN classification target. We found the base EEGNet to reach classification levels well above chance, with similar performance to previously reported P300 results. We found performance to be robust to noise, with classification similar for walking and loaded walking, with respect to standard seated condition with minimal movement. With additional architectural search and tuning to the EEGNet model (termed Cog-Neuro, herein; CN-EEGNet), we reached classification accuracy of greater than 95%, similar to previously reported state of the art levels in seated P300 tasks. To our knowledge, these results are the first documented implementation of a DNN for the classification of cognitive neural state during dual-task walking. The classification of one's ongoing cognitive state during a demanding physical task establishes the utility for BCI in complex environments.

翻译：深度神经网络（DNN）在脑机接口（BCI）技术中的应用日益广泛，其初始目标是以计算机可读格式对人类生理信号进行分类。尽管目前对DNN在BCI中的应用认知前景广阔，但在解读动态自由移动情境下的神经事件方面经验尚浅。本研究采用改进版EEGNet，旨在对受试者在跑步机上同步行走（有时背负相当于自身体重40%的背包）时的脑电图（EEG）信号进行认知事件分类。行走中的受试者同时执行视觉奇异目标检测任务，诱发P300事件相关电位（ERP），并将其作为DNN分类目标。我们发现基础EEGNet的分类水平远超随机概率，其性能与先前报道的P300结果相近。进一步分析表明，该模型对噪声具有鲁棒性：与标准低运动坐姿条件相比，行走及负重行走状态下的分类表现基本一致。通过对EEGNet模型进行额外架构搜索与调优（本文命名为Cog-Neuro模型，即CN-EEGNet），分类准确率达到95%以上，与已报道的坐姿P300任务最优水平相当。据我们所知，本研究首次实现了DNN在双任务行走过程中对认知神经状态的分类。在复杂体力任务中实时分类个体认知状态的能力，验证了BCI在复杂环境中的实用价值。