UCLA

Traditional input methods to interface with computer systems prove to be challenging for individuals with amputations or paralysis. Although several brain-machine interfaces were developed to address this problem, their invasive nature prevents widespread adoption. Alternatively, developing interfaces using non-invasive signals has been shown to be effective but they require large, non-intuitive gestures to function. In this work, we propose a framework to decode the subtle finger movements that occur naturally during typing via analyzing non-invasive EMG signals. Here, we establish synchronized communication with an amplifier to get signal recordings, perform signal preprocessing and utilize deep learning architectures for feature extraction and classification. Our approach achieves a within-session accuracy of up to 89.23% in detecting individual finger movements during a randomized typing task, with an average accuracy of 77.64% across all sessions. The time needed for classification is 4.16 ms per sample, making our framework suitable for real-time operation. Our framework demonstrates the possibility of identifying finger movements during typing in real-time using non-invasive EMG signals and provides a starting point for future work to allow individuals with amputations or disabilities to communicate effectively with computers.