REH-MI: EEG Motor Imagery Dataset from the Same Limb for Rehabilitation Applications

The REH-MI dataset is an EEG motor imagery (MI) dataset developed for rehabilitation-oriented research. It comprises 9,180 trials recorded using a 32-channel Emotiv EPOC Flex headset from 18 healthy, right-handed male participants across two sessions. Each trial corresponds to one of four right-hand conditions: imagined finger movement, wrist movement, elbow flexion, or rest, totaling 510 trials per subject. The data collection protocol incorporated signal quality checks, including impedance monitoring and electrode placement verification, followed by bad-trial flagging and classification-based validity checks after recording. REH-MI addresses the scarcity of same-limb MI datasets aimed at post-stroke rehabilitation and is expected to support studies in BCI-driven neurorehabilitation, assistive technology, and motor-function decoding.