Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

• Published in: Frontiers in human neuroscience Vol. 14; p. 103
• Main Authors: Xu, Lichao, Xu, Minpeng, Ke, Yufeng, An, Xingwei, Liu, Shuang, Ming, Dong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 21.04.2020; Frontiers Media S.A
• Subjects: Algorithms; brain-computer interface; Classification; cross-dataset variability; cross-subject variability; Datasets; Deep learning; EEG; Eigenvalues; Electroencephalography; Geometry; Human Neuroscience; Interfaces; Internet; Machine learning; Medical imaging; Signal processing; transfer learning; deep learning; transfer learning; cross-subject variability; cross-dataset variability; EEG; brain-computer interface

Cross-subject variability problems hinder practical usages of Brain-Computer Interfaces. Recently, deep learning has been introduced into the BCI community due to its better generalization and feature representation abilities. However, most studies currently only have validated deep learning models...