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Part 1: Document Description
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Citation |
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Title: |
Cross-Session Motor Imagery EEG dataset |
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Identification Number: |
doi:10.7910/DVN/GH74ZG |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2025-07-06 |
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Version: |
1 |
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Bibliographic Citation: |
Pan, Lincong, 2025, "Cross-Session Motor Imagery EEG dataset", https://doi.org/10.7910/DVN/GH74ZG, Harvard Dataverse, V1 |
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Citation |
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Title: |
Cross-Session Motor Imagery EEG dataset |
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Identification Number: |
doi:10.7910/DVN/GH74ZG |
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Authoring Entity: |
Pan, Lincong (https://ror.org/012tb2g32) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Pan, Lincong |
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Depositor: |
Pan, Lincong |
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Date of Deposit: |
2025-07-05 |
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Holdings Information: |
https://doi.org/10.7910/DVN/GH74ZG |
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Study Scope |
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Keywords: |
Computer and Information Science, Engineering, Medicine, Health and Life Sciences |
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Abstract: |
<h2>Abstract</h2> <p>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.</p> <h2>Dataset Composition</h2> <p>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:</p> <ul> <li>Neuroscan SynAmps2 amplifier</li> <li>28 scalp electrodes (10-20 system)</li> <li>250Hz sampling rate (after downsampling)</li> <li>Band-pass filtering (0.01-200Hz)</li> </ul> <p>Data is stored in MATLAB format with subject/session labeling.</p> <h2>Participants</h2> <p>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.</p> <h2>Experimental Paradigm</h2> <p>Referenced from: <em>Pan et al. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering</em> <a href="https://doi.org/10.1088/1741-2552/ad0a01">[DOI]</a></p> <p>Each session (~180 trials) contained three phases:</p> <ul> <li><strong>Rest Period (4s):</strong> Relaxation without mental tasks</li> <li><strong>Task Period (4s):</strong> MI execution (left/right hand)</li> </ul> <p>Conducted in a controlled environment. For Session 2:</p> <ul> <li>First 30 trials: Training</li> <li>Remaining trials: Testing with online feedback</li> </ul> <p><strong>Note:</strong> Trial counts vary across sessions and subjects.</p> <h2>Data Acquisition and Preprocessing</h2> <p>Technical specifications:</p> <ul> <li>Original sampling rate: 1000Hz</li> <li>Band-pass filter: 0.01-200Hz</li> <li>Notch filter: 50Hz (powerline noise removal)</li> <li>Final sampling rate: 250Hz (after downsampling)</li> </ul> <h2>Data Structure</h2> <p>MATLAB struct containing:</p> <ul> <li><code>data</code>: 3D matrix [n_channels × n_samples × n_trials]</li> <li><code>label</code>: Trial labels [n_trials] (1: left-hand, 2: right-hand)</li> <li><code>fs</code>: Sampling frequency (250Hz)</li> <li><code>period</code>: Trial duration (seconds)</li> <li><code>chaninfo</code>: Channel metadata (cell array)</li> </ul> |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
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. |
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Bibliographic Citation: |
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. |
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