  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.
