We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked potential (SSVEP)-based EEG and facial EMG to improve multimodal control and mitigate fatigue in assistive applications. Traditional BMIs relying solely on EEG or EMG suffer from inherent limitations; EEG-based control requires sustained visual focus, leading to cognitive fatigue, while EMG-based control induces muscular fatigue over time. Our system dynamically alternates between EEG and EMG inputs, using EEG to detect SSVEP signals at 9.75 Hz and 14.25 Hz and EMG from cheek and neck muscles to optimize control based on task demands. In a virtual turtle navigation task, the hybrid system achieved task completion times comparable to an EMG-only approach, while 90% of users reported reduced or equal physical demand. These findings demonstrate that multimodal BMI systems can enhance usability, reduce strain, and improve long-term adherence in assistive technologies.
@article{arxiv.2502.10904,
title = {Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand},
author = {Daniel Wang and Katie Hong and Zachary Sayyah and Malcolm Krolick and Emma Steinberg and Rohan Venkatdas and Sidharth Pavuluri and Yipeng Wang and Zihan Huang},
journal= {arXiv preprint arXiv:2502.10904},
year = {2025}
}
Comments
5 pages, 5 figures, submitted to IEEE Engineering in Medicine and Biology Conference (EMBC)