J Electromyogr Kinesiol. 2026 Mar 6;88:103134. doi: 10.1016/j.jelekin.2026.103134. Online ahead of print.
ABSTRACT
Human-machine interface (HMI) and brain-computer interface (BCI) are proving to help make technologies better and helpful for people with disabilities. These systems give individuals the ability to easily control wheelchair, and enhance their quality of life. This review focuses on the use of EMG (muscle activity) and EEG (brain activity) signals, considered primarily as individual modalities, for wheelchair control. EMG signals facilitate muscle control, which is particularly useful for individuals with motor impairments or impaired limb function. On the other hand, EEG-based BCIs enable independent navigation for individuals with severe motor disorders by systematically analyzing brainwave patterns. This review covers the literature from 2014 to 2024 and focuses on signal acquisition, filtering, feature extraction, and classification techniques. It also highlights the challenges of signal processing, inter-subject interaction, and real-time optimization. Based on the analyzed studies, research gaps are identified, and future directions are outlined, including the potential integration of multimodal EEG-EMG approaches as an emerging research trend for developing more user-centric and adaptive wheelchair systems.
PMID:41864054 | DOI:10.1016/j.jelekin.2026.103134