Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology
This study provides confidence for neurophysiology researchers in using DNNs for EEG data analysis, by demonstrating that DNN-identified ROIs align with established neurophysiological ROIs, despite the lack of network interpretability.
This paper investigates the alignment between regions of interest (ROIs) identified by deep neural networks (DNNs) and conventional neurophysiological methods when analyzing EEG data related to working memory. By applying an attention mechanism via global average pooling to a public EEG dataset, the study found coherence in the ROIs identified by both approaches.
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.