Exploring Gender Differences in Motor Imagery EEG for Brain-Computer Interface Applications*

Brain-computer interface (BCI) technology demonstrated immense potential across diverse fields. However, current research on electroencephalogram (EEG) commonly assumed single model can be universally applied across all gender identities. While a few studies have identified gender differences through supervised classification of resting-state EEG, the majority have largely overlooked the potential impact of gender-specific differences in BCI applications. This study explored the gender-specific differences in motor imagery (MI) within BCIs and the feasibility of gender recognition in unsupervised settings. We utilized public datasets of male and female EEG signals, applied widely used machine learning algorithms for task and gender identification. The results showed that the average MI classification accuracy for female was 0.57% higher than male, despite the dataset containing more male subjects. In addition, gender recognition accuracy from EEG MI data exceeded 97%. These findings have highlighted the importance of considering gender-specific differences in BCI research and application. The results of this study could inform the development of more personalized effective BCIs in healthcare and other fields, ultimately leading to improved outcomes and experiences for users of all genders.