Cross-Session Motor Imagery EEG dataset

Abstract The Pan2025 dataset contains electroencephalography (EEG) signals from 10 subjects performing motor imagery (MI) tasks across two experimental sessions. This dataset facilitates research on cross-session variability in MI-EEG signals and supports development of robust brain-computer interface (BCI) systems. Dataset Composition The dataset includes EEG recordings from 10 subjects across two sessions, featuring left-handed and right-handed MI tasks with visual cues. Data was acquired using: Neuroscan SynAmps2 amplifier 28 scalp electrodes (10-20 system) 250Hz sampling rate (after downsampling) Band-pass filtering (0.01-200Hz) Data is stored in MATLAB format with subject/session labeling. Participants The cohort consists of 10 individuals (3 females, 7 males) aged 22-25 years, including 2 left-handed participants. All subjects were neurologically healthy with no movement disorders. Experimental Paradigm Referenced from: Pan et al. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering [DOI] Each session (~180 trials) contained three phases: Rest Period (4s): Relaxation without mental tasks Task Period (4s): MI execution (left/right hand) Conducted in a controlled environment. For Session 2: First 30 trials: Training Remaining trials: Testing with online feedback Note: Trial counts vary across sessions and subjects. Data Acquisition and Preprocessing Technical specifications: Original sampling rate: 1000Hz Band-pass filter: 0.01-200Hz Notch filter: 50Hz (powerline noise removal) Final sampling rate: 250Hz (after downsampling) Data Structure MATLAB struct containing: data: 3D matrix [n_channels × n_samples × n_trials] label: Trial labels [n_trials] (1: left-hand, 2: right-hand) fs: Sampling frequency (250Hz) period: Trial duration (seconds) chaninfo: Channel metadata (cell array)