Non-Invasive EEG-Based Brain-Computer Interface for Real-Time Prosthetic Hand Control

This paper highlights a non-invasive EEG-controlled brain–computer interface BCI system for real-time control over a developed prosthetic hand system. Motor imagery trials involving opening, closing, grasping, and resting states were conducted using a single-channel NeuroSky MindWave helmet to produce EEG signals. These EEGs were processed in real-time to extract features and employ machine learning to decode intentions and control outputs. This system proved quite effective in terms of classification accuracy and precision, at approximately 87% and 88%, respectively, with a very short latency to enable smooth control over the prosthetic hand system. By comparing this system to conventional EMG technology or using invasive electrode technology, this technology prioritizes ease of use and a sense of safety and usability in patients. This system proved very effective in controlling the prosthetic hand to perform intended tasks, thereby suggesting a potential improvement in practical usability and feasibility in terms of quality-of-life enhancement in patients with lost upper limbs.