Cross-Session Motor Imagery EEG dataset

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Pan, Lincong, 2025, "Cross-Session Motor Imagery EEG dataset", https://doi.org/10.7910/DVN/GH74ZG, Harvard Dataverse, V1
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Description

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)
(2025-07-05)
Subject Engineering; Medicine, Health and Life Sciences; Computer and Information Science
Related Publication References: Pan, L., Wang, K., Xu, L., Sun, X., Yi, W., Xu, M. & Ming, D. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering, 20(6), 066011.https://doi.org/10.1088/1741-2552/ad0a01
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