An EEG-EMG Dataset from a Standardized Reaching Task for Biomarker Research in Upper Limb Assessment

This work introduces a bimodal dataset designed to explore electrophysiological biomarkers for assessing assistive technologies in neurorehabilitation. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks assisted by an upper-limb exoskeleton. To standardize and simulate natural upper-limb movements relevant to daily activities, a custom-designed touch panel was used. High-density EEG (hd-EEG) and surface EMG (sEMG) were recorded to capture neuromechanical responses. The dataset adheres to Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. We provide subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency-domain decomposition for EMG. Beyond evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.