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Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology

2021-02-06 09:09:57
Yurui Ming
arXiv_CV
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.10994

PDF

https://arxiv.org/pdf/2102.10994.pdf

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