Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology

TL;DR

This study explores the coherence between deep neural network regions and neurophysiological regions in EEG data for working memory, demonstrating alignment and potential for DNN-based EEG analysis.

Contribution

It introduces a method to compare DNN and neurophysiological ROIs in EEG data, showing their alignment in working memory analysis.

Findings

DNN ROIs align with neurophysiological ROIs in EEG data

Attention mechanism via GAP reveals coherent regions

Supports DNN's potential in EEG analysis

Abstract

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.