The Coherence of the Working Memory Study Between Deep Neural Networks and Neurophysiology: Insights From Distinguishing Topographical Electroencephalogram Data Under Different Workloads

Abstract

The automatic feature-extraction capability of deep neural networks (DNNs) endows them with the potential for analyzing complicated electroencephalogram (EEG) data captured from brain functionality research. This article investigates the potential coherent correspondence between the region of interest (ROI) for DNNs to explore, and the ROI for conventional neurophysiological-oriented methods to work with, as exemplified in the case of a working memory study. The attention mechanism induced by global average pooling (GAP) is applied to a public EEG data set of a working memory test to unveil these coherent ROIs via a classification problem. The results show the potential alignment of the ROIs from different discipline methods, and consequently asserts the confidence and promise of utilizing DNNs for EEG data analysis.