Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand
Journal:
arXiv
Published Date:
Feb 15, 2025
Abstract
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.
