Easy-to-use EMG system to decode hand movements in people with spinal cord injury.

Objective Loss of hand function following spinal cord injury (SCI) severely impacts independence and quality of life. Restoring volitional hand control in individuals with SCI remains a critical challenge, addressed using different approaches, including physiotherapy, occupational therapy, and assistive technologies such as neuroprostheses and robotic systems. This study aimed to develop and evaluate an easy-to-setup and user-friendly EMG system for decoding attempted hand and finger movements in individuals with SCI. Approach We implemented an EMG decoding system for hand opening/closing (2 classes) and single-finger flexion (3 classes) attempts, using a fast-donning 32-channel dry EMG sleeve integrated with a seamless deep-learning LSTM-based decoding pipeline. Eight people with different classifications of SCI tested the system over two sessions while receiving real-time feedback through a virtual hand interface. Main results The system achieved high online accuracy for hand open-close discrimination across all subjects (mean 92.5%), and an accuracy that was consistently above chance for single-finger flexion classification (mean 74.2%), with a greater error observed for more nuanced movements in participants with higher motor impairment. Performance plateaued quickly over time, indicating that user learning to not play a relevant role within the limited number of sessions performed. Significance These findings demonstrate that our system allows good online decoding of different hand and finger movements in people with SCI. The approach supports pathways toward intuitive, digit-level control of neuroprostheses and robotic devices, with strong potential for clinical translation due to its quick setup and ease of use.