Artifact and Epoching Steps

After basic preprocessing, AutoClean tasks apply a sequence of functions that tag artifacts, run ICA, and prepare epochs for analysis.

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Annotate noisy epochs

Flags high-variance segments by temporarily epoching the continuous signal and measuring channel standard deviations. Technical details: Custom algorithm that uses an interquartile range (IQR) rule to mark epochs where many channels have outlier variance. View Source Code → Parameters

• epoch_duration, epoch_overlap – size and overlap of temporary epochs in seconds.
• quantile_k – IQR multiplier for outlier detection.
• quantile_flag_crit – proportion of channels required to flag an epoch.
• annotation_description – text for added annotations.

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Annotate uncorrelated epochs

Identifies periods where channels lose spatial correlation with their neighbors. Technical details: Custom routine that computes nearest-neighbor correlations using channel locations; epochs with too many low-correlation channels are annotated. View Source Code → Parameters

• epoch_duration, epoch_overlap – epoch size and overlap in seconds.
• n_nearest_neighbors – number of spatial neighbors per channel.
• corr_method, corr_trim_percent – aggregation strategy for neighbor correlations.
• outlier_k, outlier_flag_crit – thresholds for marking epochs.
• annotation_description – text for added annotations.

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Detect dense oscillatory artifacts

This step looks for short bursts of oscillations that appear across many channels simultaneously. These patterns often correspond to what some researchers call reference artifacts — global signals that arise not from neural activity but from the reference montage or amplifier coupling. They can appear as large-amplitude “waves” shared across nearly every electrode at once.

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Technical details

AutoCleanEEG uses a custom sliding-window algorithm:

• For each window, it counts how many channels exceed a peak-to-peak amplitude threshold.
• If the number of channels crossing the threshold is above a specified minimum, the window is annotated as an artifact.
• Optional padding ensures the artifact window includes pre- and post-burst activity for safety.

View Source Code → Parameters

• window_size_ms – window length in milliseconds.
• channel_threshold_uv – per-channel peak-to-peak amplitude threshold (µV).
• min_channels – minimum number of channels exceeding the threshold.
• padding_ms – padding added before/after detected artifacts.
• annotation_label – description for annotations.

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Run ICA

Decomposes the signal into independent components for later artifact rejection. Technical details: run_ica wraps autoclean.functions.ica.fit_ica, which uses mne.preprocessing.ICA and optional temporary high-pass filtering. View Source Code → Parameters

• method, n_components, fit_params – passed to MNE’s ICA.
• temp_highpass_for_ica – optional high-pass filter applied only for decomposition.

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Classify and reject components

Labels ICA components as brain or artifact and optionally removes the bad ones. Technical details: classify_ica_components calls AutoClean’s classifier wrappers (ICLabel, ICVision, or a hybrid of both). apply_ica_component_rejection then drops components exceeding an optional probability threshold. View Classification Code → | View Rejection Code → Parameters

• method – 'iclabel' (machine learning), 'icvision' (custom template matching), or 'hybrid' (ICLabel followed by ICVision on a subset).
• ic_flags_to_reject – component labels considered artifacts.
• ic_rejection_threshold – minimum probability for rejection.
• psd_fmax – max frequency in power spectrum plots (ICVision only).
• icvision_n_components – number of leading components to inspect with ICVision when using the hybrid method.

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Create regular epochs

Splits continuous data into fixed-length segments for analysis. Technical details: Wrapper around autoclean.functions.epoching.create_regular_epochs, which builds events and constructs an mne.Epochs object while tracking metadata. View Source Code → Parameters

• tmin, tmax – epoch boundaries in seconds.
• remove_baseline.window – baseline interval if enabled.
• threshold_rejection.volt_threshold – amplitude rejection threshold.
• reject_by_annotation – drop epochs overlapping bad annotations.

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Detect outlier epochs

Removes epochs with abnormal amplitude or variance. Technical details: detect_outlier_epochs implements a FASTER-inspired z-score approach across mean, variance, maximum, and range metrics. View Source Code → Parameters

• threshold – z-score required to mark an epoch as an outlier.

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GFP clean epochs

Drops epochs with extreme Global Field Power (GFP) values and optionally subsamples to a fixed count. Technical details: gfp_clean_epochs computes GFP across scalp electrodes and removes epochs whose GFP z-score exceeds a threshold. View Source Code → Parameters

• gfp_threshold – z-score cutoff.
• number_of_epochs – randomly retain this many cleaned epochs.
• random_seed – seed used when selecting epochs.

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Generate reports

Creates before/after visualizations for quality control. Technical details: Combines mixin helpers like plot_raw_vs_cleaned_overlay and step_psd_topo_figure to save diagnostic figures. View Report Generation → | View PSD Figures →