A new Multi-modal dataset comprising neural EEG signals and kinematic data associated with three hand movements — open/close, finger tapping, and wrist rotation — along with a rest period. The dataset, obtained from eleven subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (~ 1 minute) while introducing more noise than the gold standard devices for data acquisition. The data set, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. For citation please refer to the paper:Mattei, E.; Lozzi, D.; Di Matteo, A.; Cipriani, A.; Manes, C.; Placidi, G. MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors 2024,24, 5207. https://doi.org/10.3390/s24165207 References: Placidi, Giuseppe. "A smart virtual glove for the hand telerehabilitation." Computers in Biology and Medicine 37.8 (2007): 1100-1107. Placidi, Giuseppe, et al. "Measurements by a LEAP-based virtual glove for the hand rehabilitation." Sensors 18.3 (2018): 834. Placidi, Giuseppe, et al. "Patient–therapist cooperative hand telerehabilitation through a novel framework involving the virtual glove system." Sensors 23.7 (2023): 3463.

Sept. 2025The Python script and the notebook for generating the FIF and SET files with the corresponding annotations were added.