Identifying Hand-based Input Preference Based on Wearable EEG

Understanding user input preference can improve the user experience, however automatically determining preference can be difficult. In this paper, we designed an EEG-based method for directly evaluating hand-based input preference for touch and mid-air gestures on a smartwatch. We conducted a two-phase experiment, recording EEG data from 18 participants as they performed gestures and captured their ratings (Phase 1) and preference choices (Phase 2) for each gesture. Our analysis uncovered distinct EEG patterns between preferred and non-preferred gestures, including significant differences in Power Spectral Density (PSD), Coherence (Coh), and Sample Entropy (SE) features. When participants engaged with their preferred input gestures, we identified decreased brain activity (PSD) in the central and occipital regions, reduced brain connectivity (Coh) in the delta and alpha bands, and increased brain complexity (SE) in multiple sizes. These insights offer the potential to develop rapid detection of user intent for interactive computing devices by analysing brain signals.