Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 6 Feb 2021.
Builds on
6 of 25 references.
References · 6
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2015Learning Deep Features for Discriminative Localizationcited by 2,436
- 2013Visualizing and Understanding Convolutional Networkscited by 2,290
- 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
- 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
- 2015Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networkscited by 45
Built on by
Not cited yet.