Mind the Cap: Inclusivity Gaps in EEG Research

Neuroscience is posited to be generalisable and unbiased, yet recent reviews highlight the continued underrepresentation of minority racial groups in EEG research. Among the most frequently identified methodological barriers to inclusion are the hair types, styles, and volume prevalent in Black individuals, assumed to substantially increase cap-fitting difficulty and compromise data quality. Yet, while proposed solutions often put the onus on participants (i.e., hairstyle changes), there is-to our knowledge-no current evidence demonstrating EEG data quality variation as a product of race. In the present study we analyzed data from wet and dry EEG systems to explore how race (Black/White) and/or gender (female/male) impact data quality. Dry EEG data was collected at a university in the UAE (N = 60, 30F, 30 M, 30 Black, 30 White) and wet EEG data was generously shared by Pech and Caspar (2024) (N = 53, 36F, 17 M, 27 Black, 26 White). Five data quality metrics were analyzed: bad electrode count, ICA decomposition quality, artifact rejection rate, baseline standard deviation, and standardized measurement error. We found no evidence of inferior data quality in Black participants across any measure or either system. In contrast, female (vs. male) participants presented with poorer data quality in metrics from both system types. While qualitative experimenter data acquisition notes (available for the dry EEG system data) suggest that EEG capping may prove more challenging with Black (vs White) participants, this does not appear to translate to a reduction in data quality. Our findings suggest that good quality data can be collected from Black participants across a range of EEG systems, and that the development of new methods or techniques is not necessarily a prerequisite to greater representation in EEG research.