Standalone Functions (autoclean.functions)#

This section covers the standalone functions that provide modular EEG data processing capabilities in AutoClean. These functions can be used independently without the Task/Mixin framework, making them ideal for custom workflows, scripting, and integration with other EEG analysis pipelines.

Standalone functions are designed to be stateless and operate directly on MNE data objects:

from autoclean.functions import filter_data, detect_bad_channels, fit_ica

# Direct function usage - no task object required
filtered_raw = filter_data(raw, l_freq=1.0, h_freq=40.0)
bad_channels = detect_bad_channels(filtered_raw)
ica = fit_ica(filtered_raw)

Note: Standalone functions return processed data objects and do not modify the input data in-place. They provide a functional programming approach as an alternative to the object-oriented Task/Mixin pattern.

Available Function Categories#

Preprocessing Functions#

`filtering.filter_data`	Filter EEG data using highpass, lowpass, and/or notch filtering.
`referencing.rereference_data`	Apply referencing scheme to EEG data.
`resampling.resample_data`	Resample EEG data to a new sampling frequency.
`basic_ops.crop_data`	Crop EEG data to a specific time range.
`basic_ops.drop_channels`	Drop channels from EEG data.

Artifact Detection Functions#

`channels.detect_bad_channels`	Detect bad channels using multiple statistical methods.
`channels.interpolate_bad_channels`	Interpolate bad channels using spherical spline interpolation.

Epoching Functions#

`regular.create_regular_epochs`	Create regular fixed-length epochs from continuous EEG data.
`statistical.create_sl_epochs`	Create statistical learning epochs based on syllable event patterns.
`eventid.create_eventid_epochs`	Create epochs based on specific event IDs from continuous EEG data.
`quality.detect_outlier_epochs`	Detect and mark outlier epochs based on statistical measures.
`quality.gfp_clean_epochs`	Clean epochs based on Global Field Power (GFP) outlier detection.

ICA Functions#

`ica_processing.fit_ica`	Fit Independent Component Analysis (ICA) to EEG data.
`ica_processing.classify_ica_components`	Classify ICA components using automated algorithms.
`ica_processing.apply_ica_rejection`	Apply ICA to remove specified components from EEG data.

Segment Rejection Functions#

`segment_rejection.annotate_noisy_segments`	Identify and annotate noisy segments in continuous EEG data.
`segment_rejection.annotate_uncorrelated_segments`	Identify and annotate segments with poor channel-neighbor correlations.
`dense_oscillatory.detect_dense_oscillatory_artifacts`	Detect smaller, dense oscillatory multichannel artifacts.

Advanced Processing Functions#

autoreject.autoreject_epochs

Apply AutoReject for automatic epoch cleaning and channel interpolation.

Visualization Functions#

`plotting.plot_raw_comparison`	Plot raw data comparison showing original vs cleaned data overlay.
`plotting.plot_ica_components`	Plot ICA component topographies and properties.
`plotting.plot_psd_topography`	Plot power spectral density topographical maps for frequency bands.
`reports.generate_processing_report`	Generate a comprehensive HTML processing report.
`reports.create_processing_summary`	Create a JSON summary of processing steps.