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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18630095 |
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Table of Contents:
- <h1>SENSI EEG PREPROC — Bad-Channel Detection Module</h1> <h2>Version v1.0.0 — Initial Public Release</h2> <p>This is the first public release of the Bad-Channel Detection Module.</p> <p>This version corresponds to the codebase described in the accompanying preprint:</p> <blockquote> <p>Amilcar J. Malave and Blair Kaneshiro (2026). <em>EEG Bad-Channel Detection Using Multi-Feature Thresholding and Co-Occurrence of High-Amplitude Transients.</em> bioRxiv. https://doi.org/10.64898/2026.02.04.703874</p> </blockquote> <p>Dataset:</p> <blockquote> <p>Amilcar J. Malave and Blair Kaneshiro (2025). <em>Example EEG data for the SENSI EEG PREPROC Bad-Channel Detection Module</em> [Data set]. Stanford Digital Repository. https://doi.org/10.25740/dg856vy8753</p> </blockquote> <h2>Included in this Release</h2> <ul> <li><code>markSusChs.m</code> (main user entry point)</li> <li>Multi-feature suspiciousness scoring:<ul> <li>Neighbor dissimilarity</li> <li>Amplitude screening</li> <li>Variance-based measures</li> </ul> </li> <li>High-amplitude transient clustering via Jaccard-like similarity</li> <li>Interactive review interface (<code>reviewBadChsUI</code>)</li> <li>Example workflow (<code>example.m</code>)</li> <li>User Manual (PDF)</li> </ul> <h2>Intended Usage</h2> <p>This Module is designed as a quality-control step prior to ICA and downstream EEG analyses.<br> It emphasizes interpretability and human-in-the-loop validation rather than fully automated rejection.</p> <h2>MATLAB Requirements</h2> <ul> <li>MATLAB R2024b (tested)</li> <li>Statistics and Machine Learning Toolbox</li> </ul> <h2>Notes</h2> <p>This release represents the first stable public version of the Module.<br> Future releases may refine clustering behavior, visualization outputs, and parameter defaults.</p> <p>This version should be cited when referencing the v1.0.0 implementation.</p>