EMG-EEG dataset for Upper-Limb Gesture Classification

Electromyography (EMG) has limitations in human machine interface due to disturbances like electrode-shift, fatigue, and subject variability. A potential solution to prevent model degradation is to combine multi-modal data such as EMG and electroencephalography (EEG). This study presents an EMG-EEG dataset for enhancing the development of upper-limb assistive rehabilitation devices. The dataset, acquired from thirty-three volunteers without neuromuscular dysfunction or disease using commercial biosensors is easily replicable and deployable. The dataset consists of seven distinct gestures to maximize performance on the Toronto Rehabilitation Institute hand function test and the Jebsen-Taylor hand function test. The authors aim for this dataset to benefit the research community in creating intelligent and neuro-inspired upper limb assistive rehabilitation devices.