Malave, A. J. | Search bioRxiv

bioRxiv2026

Semi-Automated Identification of EKG and Trigger Artifacts in EEG Using ICA and Spectral Characteristics

A persistent bottleneck in post-Independent Component Analysis (ICA) Electroencephalogram (EEG) preprocessing is the manual identification of artifact components for removal. In practice, this step can be slow, subjective, and difficult to standardize, particularly for cardiac contamination and trigger-related leakage, where artifact structure may be distributed across multiple components or appear outside the highest-variance Independent Components (ICs). We developed the SENSI-EEG-Preproc-ICA-EKG-Trigger Module to make this stage faster and more reproducible without removing the user from the decision process. The Module is a semi-automated MATLAB framework for post-ICA screening of cardiac and trigger-related artifact components using spectral characteristics. EKG candidates are prioritized by detecting harmonic structure around a physiologically plausible heart-rate fundamental, whereas trigger-related candidates are prioritized by measuring harmonic concentration at frequencies determined by the known repetition period of the trigger sequence. The resulting candidates are then reviewed in dedicated interfaces that present scalp topography, time-domain activity, and frequency-domain structure together, allowing the final classification to be confirmed or corrected by the user. In this way, the Module narrows the search space while preserving interpretability and explicit human control over the final keep/remove decision. The release includes a public codebase, a user manual, example workflows, and an accompanying example dataset. This paper presents the Module as a practical methods-and-software contribution for post-ICA EEG cleaning.

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bioRxiv2026

EEG Bad-Channel Detection Using Multi-Feature Thresholding and Co-occurrence of High-Amplitude Transients

Bad channels in electroencephalography (EEG) recordings can substantially degrade downstream analyses, particularly in high-density datasets where localized hardware or motionrelated artifacts may affect groups of electrodes in a structured manner. We introduce a MATLAB Module for bad-channel quality control that emphasizes interpretability, relational structure, and human-in-the-loop validation rather than fully automated rejection. The method operates on multichannel EEG data and combines complementary channel-level features, including time-dependent neighbor dissimilarity and amplitude- and variance-based statistics to score and pre-label channels as good, suspicious, or bad. To expose shared artifactual structure, channels are additionally grouped using a similarity measure derived from the co-occurrence of robustly detected high-amplitude transients, allowing channels to be reviewed together. Importantly, clustering is used as an exploratory tool to reveal co-artifactual patterns rather than to impose final class labels, which are confirmed through an interactive review interface supported by summary visualizations and grouped channel displays. This Module is released as a publicly available codebase with documentation, example work-flows, and a supporting dataset. This Module is designed as a quality-control step preceding ICA and does not replace end-to-end data cleaning pipelines, which typically involve additional steps such as interpolation of known bad channels.

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