Kaining Zhang∗
Understanding user input preference can improve the user experi- ence, however automatically determining preference can be diffi- cult. In this paper, we designed an EEG-based method for directly evaluating hand-based input preference for touch and mid-air ges- tures on a smartwatch. We conducted a two-phase experiment, recording EEG data from 18 participants as they performed ges- tures 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 en- gaged 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. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. AHs 2024, April 04–06, 2024, Melbourne, VIC, Australia © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0980-7/24/04 https://doi.org/10.1145/3652920.3653028 CCS CONCEPTS • Human-centered computing →User studies; Gestural input; Laboratory experiments.