Adversarial invariant feature learning is applied to one-hour EEG speller data to remove drowsiness effects from event-related features using recording block order as the nuisance variable.
Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface
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Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects
Adversarial invariant feature learning is applied to one-hour EEG speller data to remove drowsiness effects from event-related features using recording block order as the nuisance variable.