Self-Supervised Learning for Electroencephalography

Mohammad Hossein Rafiei, Lynne V. Gauthier, Hojjat Adeli et al.

2022 · IEEE Transactions on Neural Networks and Learning Systems · 318 citations

Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG…

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