# Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects

## TL;DR

This paper introduces an adversarial feature learning approach to mitigate drowsiness effects in EEG-based BCIs, improving stability by learning nuisance-invariant features during long recording sessions.

## Contribution

It proposes a novel adversarial invariant feature learning method as regularization for EEG deep learning models to reduce variability caused by drowsiness.

## Key findings

- Successfully eliminated drowsiness effects from EEG features
- Demonstrated feasibility on long-duration EEG recordings
- Improved BCI performance stability

## Abstract

Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs). Specifically, gradual changes in fatigue and vigilance levels during long EEG recording durations and BCI system usage bring along significant fluctuations in BCI performances even when these systems are calibrated daily. We address this in an experimental offline study from EEG-based BCI speller usage data acquired for one hour duration. As the main part of our methodological approach, we propose the concept of adversarial invariant feature learning for BCIs as a regularization approach on recently expanding EEG deep learning architectures, to learn nuisance-invariant discriminative features. We empirically demonstrate the feasibility of adversarial feature learning on eliminating drowsiness effects from event related EEG activity features, by using temporal recording block ordering as the source of drowsiness variability.