The auto feature extraction capability of deep neural networks (DNN) endows them the potentiality for analysing complicated electroencephalogram (EEG) data captured from brain functionality research. This work investigates the potential coherent correspondence between the region-of-interest (ROI) for DNN to explore, and ROI for conventional neurophysiological oriented methods to work with, exemplified in the case of working memory study. The attention mechanism induced by global average pooling (GAP) is applied to a public EEG dataset of working memory, to unveil these coherent ROIs via a classification problem. The result shows the alignment of ROIs from different research disciplines. This work asserts the confidence and promise of utilizing DNN for EEG data analysis, albeit in lack of the interpretation to network operations.

翻译：深神经网络(DNN)的自动特征提取能力赋予它们分析从大脑功能研究中获取的复杂电子脑图数据的潜力,这项工作调查了DNN进行探索的“利益区域”与常规神经生理方法合作的“目标区域”之间可能一致的对应关系,如工作记忆研究。全球平均集合引出的注意机制适用于工作记忆的“公共电子脑图”数据集,通过分类问题公布这些一致的“目标区域”数据。结果显示不同研究学科的“目标区域”对准了研究学科的“目标区域”数据,这项工作表明,尽管对网络运作缺乏解释,但利用“目标区域”数据网络数据分析的信心和前景。