Mind the Cap: Inclusivity Gaps in EEG Research

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Underrepresentation of minority racial groups in neuroscience research, particularly in electroencephalography (EEG).
• Assumed methodological barriers related to Black individuals' hair types, styles, and volume potentially compromising EEG data quality.
• Lack of empirical evidence supporting the claim that race impacts EEG data quality.

Purpose of the Study:

• To investigate the impact of race (Black/White) and gender (female/male) on EEG data quality using both wet and dry EEG systems.
• To determine if perceived challenges in EEG cap fitting for Black individuals translate to reduced data quality.
• To provide evidence-based insights into improving inclusivity in EEG research.

Main Methods:

• Analysis of EEG data from two systems: dry EEG (N=60, UAE) and wet EEG (N=53, shared data).
• Inclusion of participants stratified by race (Black/White) and gender (female/male).
• Evaluation of five data quality metrics: bad electrode count, ICA decomposition quality, artifact rejection rate, baseline standard deviation, and standardized measurement error.

Main Results:

• No significant differences in EEG data quality were found between Black and White participants across any metric or system.
• Female participants exhibited poorer data quality compared to male participants across both EEG system types.
• Qualitative notes indicated potential challenges in EEG cap fitting for Black participants, but this did not affect data quality.

Conclusions:

• Perceived difficulties in EEG cap fitting for Black individuals do not result in inferior data quality.
• Good quality EEG data can be obtained from Black participants without requiring new methods or techniques.
• Findings challenge assumptions hindering minority inclusion and suggest that current EEG methods are suitable for diverse populations.