Window-level, per-channel normalization helps supervised EEG tasks, while minimal or cross-channel window normalization suits contrastive self-supervised learning on EEG.
Overall these findings show that across tasks, better results were obtained when normalization is done on the window level
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Data Normalization Strategies for EEG Deep Learning
Window-level, per-channel normalization helps supervised EEG tasks, while minimal or cross-channel window normalization suits contrastive self-supervised learning on EEG.