API Reference

This section contains the complete API documentation for eegprep. The API is organized into logical modules covering core functionality, preprocessing, independent component analysis, signal processing, input/output operations, and utility functions.

• Core Functions and Classes
  • Main Pipeline
  • Data Validation
  • Object-Oriented Interface
• Preprocessing Functions
  • Artifact Removal
  • Channel Operations
  • Signal Processing
• Independent Component Analysis
  • ICA Decomposition
  • Component Classification
  • Feature Extraction
• Signal Processing Functions
  • Spectral Analysis
  • Resampling
  • Baseline Removal
• Input/Output Functions
  • BIDS Loading
  • EEGLAB Format
  • Format Conversion
• Utility Functions
  • Data Comparison
  • Channel Operations
  • Visualization
  • Utility Modules

Core Classes

eegprep.EEGobj(EEG_or_path)

Wrapper class for EEG datasets stored as dictionaries.

Data Loading and Saving

eegprep.pop_loadset([file_path])	Load EEGLAB dataset from .set or .mat file.
eegprep.loadset(*args, **kwargs)	Wrap pop_loadset.
eegprep.pop_loadset_h5(file_name)	Load EEG data from HDF5 file.
eegprep.pop_saveset(EEG, file_name)	Save EEG data to file.
eegprep.pop_load_frombids(filename, *[, ...])	Load an EEG data file of a supported format from a BIDS dataset.

Preprocessing Functions

Artifact Removal

eegprep.clean_artifacts(EEG[, ...])	All-in-one artifact removal, port of MATLAB clean_artifacts.
eegprep.clean_asr(EEG[, cutoff, window_len, ...])	Run the Artifact Subspace Reconstruction (ASR) method on EEG data.
eegprep.clean_flatlines(EEG[, ...])	Remove (near-) flat-lined channels.
eegprep.clean_drifts(EEG[, transition, ...])	Remove drifts from the data using a forward-backward high-pass filter.
eegprep.clean_windows(EEG[, ...])	Remove periods with abnormally high-power content from continuous data.

Channel Operations

eegprep.clean_channels(EEG[, ...])	Remove channels with problematic data from a continuous data set.
eegprep.clean_channels_nolocs(EEG[, ...])	Remove channels with abnormal data from a continuous data set.
eegprep.eeg_interp(EEG, bad_chans[, method, ...])	Interpolate missing or bad EEG channels using spherical spline.
eegprep.pop_reref(EEG, ref)	Re-reference EEG data to average reference.

Signal Processing

eegprep.pop_resample(EEG, freq[, engine])	Resample EEG data to a new sampling rate.
eegprep.pop_eegfiltnew(EEG[, locutoff, ...])	Filter EEG data using EEGLAB's pop_eegfiltnew.
eegprep.eeg_picard(EEG[, engine, posact, ...])	Perform ICA decomposition using Picard algorithm.

Independent Component Analysis

eegprep.iclabel(EEG[, algorithm, engine])	Apply ICLabel to classify independent components.
eegprep.ICL_feature_extractor(EEG[, ...])	Extract features for ICLabel classification.

Spectral Analysis

eegprep.eeg_rpsd(EEG[, nfreqs, pct_data])	Compute relative power spectral density for ICA components.
eegprep.eeg_autocorr(EEG[, pct_data])	Compute autocorrelation of ICA components.
eegprep.eeg_autocorr_welch(EEG[, pct_data])	Compute autocorrelation of EEG ICA components using Welch method.
eegprep.eeg_autocorr_fftw(EEG[, pct_data])	Compute autocorrelation of EEG ICA components using FFT.

Epoching and Selection

eegprep.pop_epoch(EEG[, types, lim])	Convert a continuous EEG dataset to epoched data by extracting data epochs time locked to specified event types or event indices.
eegprep.pop_select(EEG, **kwargs)	Python port of EEGLAB's pop_select for dict-based EEG.
eegprep.eeg_eegrej(EEG, regions)	Reject EEG data segments specified by regions.
eegprep.eegrej(*args, **kwargs)	Wrap eeg_eegrej.

Visualization

eegprep.topoplot(datavector, chan_locs, **kwargs)

Plot a 2D topographic map of EEG data.

Format Conversion

eegprep.eeg_mne2eeg(raw)	Convert MNE Raw object to EEG data structure.
eegprep.eeg_mne2eeg_epochs(epochs, ica)	Convert MNE epochs with ICA to EEGLAB dataset format.
eegprep.eeg_eeg2mne(EEG)	Convert EEG data structure to MNE Raw object.

Utilities

eegprep.eeg_checkset(EEG[, load_data])	Validate and set up EEG dataset structure.
eegprep.eeg_compare(eeg1, eeg2[, ...])	Compare two EEG-like structures, reporting differences to stderr.
eegprep.eeg_decodechan(chanlocs, chanstr[, ...])	Resolve channel identifiers to 0-based indices and labels.
eegprep.eeg_lat2point(lat_array, ...[, timeunit])	Convert latencies in time units (relative to per-epoch time 0) to latencies in data points assuming concatenated epochs (EEGLAB style).
eegprep.eeg_point2lat(lat_array[, ...])	Convert event latencies in data points to latencies in time units (default seconds).
eegprep.bids_list_eeg_files(root[, ...])	Return a list of all EEG raw-data files in a BIDS dataset.
eegprep.bids_preproc(root, *[, ...])	Apply data cleaning to EEG files in a BIDS dataset.

BIDS Pipeline

eegprep.bids_preproc(root, *[, ...])	Apply data cleaning to EEG files in a BIDS dataset.
eegprep.bids_list_eeg_files(root[, ...])	Return a list of all EEG raw-data files in a BIDS dataset.
eegprep.pop_load_frombids(filename, *[, ...])	Load an EEG data file of a supported format from a BIDS dataset.

Configuration

eegprep.EEG_OPTIONS

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).