Is Limited Participant Diversity Impeding EEG-based Machine Learning?
This addresses a critical data diversity problem for EEG-based ML applications in neuroscience and clinical settings, though it is incremental in analyzing existing methods.
The study investigated how limited participant diversity affects EEG-based machine learning model performance, finding that scaling is severely constrained by participant distribution shifts and providing actionable guidance for data collection and ML strategies.
The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and diversity of training data. It is common practice to split EEG recordings into small segments, thereby increasing the number of samples substantially compared to the number of individual recordings or participants. We conceptualise this as a multi-level data generation process and investigate the scaling behaviour of model performance with respect to the overall sample size and the participant diversity through large-scale empirical studies. We then use the same framework to investigate the effectiveness of different ML strategies designed to address limited data problems: data augmentations and self-supervised learning. Our findings show that model performance scaling can be severely constrained by participant distribution shifts and provide actionable guidance for data collection and ML research. The code for our experiments is publicly available online.