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
  • Interactive Session
  • 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
  • Time-Frequency And Statistics
  • Browser
  • Resampling
  • Baseline Removal
  • Topography
• Statistics Functions
  • Condition Tests
  • Multiple Comparisons And Surrogates
  • Data Helpers
• Input/Output Functions
  • BIDS Loading
  • Generic Import
  • Channel Locations
  • Long-Tail Import Helpers
  • EEGLAB Format
  • Text And External Export
  • History And STUDY Files
  • Format Conversion
• Interactive Pop Workflow API
  • Coverage Notes
• Extension SDK and Registry
• Minimal extension entry point
• Status model
• Catalog and Governance
• Author Test Harness
• Extension Manager and catalog
• Authoring examples
• API Reference
• Utility Functions
  • Data Comparison
  • Channel Operations
  • Visualization
  • Utility Modules

Core Classes

eegprep.EEGobj(EEG_or_path)	Wrapper class for EEG datasets stored as dictionaries.
eegprep.EEGPrepSession([EEG, ALLEEG, ...])	EEGLAB-like GUI state without module globals.

Interactive GUI and Console

eegprep.gui([onearg, session, show, block, ...])	Start the EEGPrep GUI.
eegprep.eeglab([onearg, session, show, ...])	Start the EEGPrep EEGLAB-style main window.
eegprep.EEGPrepConsoleWorkspace(session, *)	Synchronize an IPython namespace with an EEGPrepSession.
eegprep.ConsolePopResult(eeg, command, *[, ...])	Compact, unpackable result for EEGPrep console pop_* calls.
eegprep.ConsoleDatasetResult(alleeg, eeg, ...)	Compact, unpackable result for dataset-list pop_* calls.
eegprep.inputgui(spec[, initial_values, ...])	Render a dialog spec and return tagged values, or None on cancel.
eegprep.listdlg2(*[, promptstring, ...])	Open an EEGLAB-like list selector.
eegprep.pophelp(function_name[, nonmatlab, ...])	Open an EEGLAB-like help browser for a function.
eegprep.DialogSpec(title, controls, ...[, ...])	Complete EEGLAB-like dialog specification.
eegprep.ControlSpec(style[, string, tag, ...])	Single EEGLAB-like GUI control.
eegprep.CallbackSpec(name[, params, ...])	Declarative callback metadata for a GUI control.

Dataset Workspace Helpers

eegprep.eeg_emptyset()	Return an empty EEG dictionary with EEGLAB core fields.
eegprep.eeg_store(ALLEEG, EEG[, storeSetIndex])	Store EEG in ALLEEG using EEGLAB-facing 1-based indices.
eegprep.eeg_retrieve(ALLEEG, index)	Return dataset(s) from ALLEEG using EEGLAB-facing 1-based indices.
eegprep.pop_newset(ALLEEG, EEG, CURRENTSET, ...)	Store or retrieve datasets following EEGLAB's pop_newset contract.
eegprep.pop_delset(ALLEEG, indices)	Delete dataset indices from ALLEEG and return a history command.
eegprep.pop_editoptions([options])	Update EEGPrep's EEGLAB-compatible options dictionary.

GUI and Session Entry Points

eegprep.select_multiple_datasets(session[, ...])

Select multiple datasets in session using EEGLAB-facing indices.

Data Loading and Saving

eegprep.pop_loadset([file_path, loadmode, ...])	Load EEGLAB dataset from .set or .mat file.
eegprep.loadset(file_path)	Load EEGLAB dataset from file (alias for 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.
eegprep.vis_artifacts(clean_eeg[, ...])	Compare a cleaned dataset against its original clean_rawdata source.
eegprep.vis_artifacts_diagnostics(clean_eeg)	Return clean_rawdata sample/channel rejection diagnostics.

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_interp([EEG, bad_elec, method, ...])	Interpolate EEG channels using EEGLAB pop_interp semantics.
eegprep.pop_reref([EEG, ref, gui, renderer, ...])	Convert an EEG dataset to average or common-reference data.

Signal Processing

eegprep.pop_resample(EEG[, freq, engine, ...])	Resample EEG data to a new sampling rate.
eegprep.pop_eegfiltnew(EEG, *args[, gui, ...])	Filter EEG data using EEGLAB's Hamming-windowed FIR defaults.
eegprep.pop_firws(EEG, *args[, gui, ...])	Filter EEG data using a windowed-sinc FIR filter.
eegprep.pop_firpm(EEG, *args[, gui, ...])	Filter EEG data using a Parks-McClellan equiripple FIR filter.
eegprep.pop_firma(EEG, *args[, gui, ...])	Filter EEG data using a moving-average FIR filter.
eegprep.pop_kaiserbeta([dev, gui, renderer, ...])	Estimate Kaiser window beta from passband ripple.
eegprep.pop_firwsord([wtype, fs, df, dev, ...])	Estimate a windowed-sinc FIR order.
eegprep.pop_firpmord([f, a, dev, fs, gui, ...])	Estimate Parks-McClellan FIR order and pass/stop weights.
eegprep.pop_xfirws(*args[, gui, renderer, ...])	Design a FIRFilt windowed-sinc FIR and optionally export an .fir file.
eegprep.firfiltreport(*args, **kwargs)	Return an EEGLAB-style FIR filter report.
eegprep.plotfresp(b[, a, nfft, fs, dir, show])	Plot impulse, step, magnitude, and phase response like FIRFilt.
eegprep.eeg_amica(EEG[, posact, sortcomps, ...])	Perform ICA decomposition using AMICA (Adaptive Mixture ICA).
eegprep.eeg_picard(EEG[, engine, posact, ...])	Perform ICA decomposition using Picard algorithm.
eegprep.eeg_runica(EEG[, posact, sortcomps])	Perform ICA decomposition using runica (infomax) 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.
eegprep.newtimef(data, frames, tlimits, srate)	Compute an EEGLAB-like ERSP/ITC time-frequency decomposition.
eegprep.newcrossf(x, y, frames, tlimits, srate)	Compute an EEGLAB-like event-related coherence decomposition.
eegprep.timefreq(data, srate, *[, frames, ...])	Return EEGLAB-like spectral estimates for each trial.
eegprep.timef(data, frames, tlimits, srate)	Compute legacy timef outputs using EEGPrep's newtimef core.
eegprep.crossf(x, y, frames, tlimits, srate)	Compute legacy crossf outputs using EEGPrep's newcrossf core.
eegprep.pac(X, Y, srate, **kwargs)	Compute phase-amplitude coupling from epoched signals.
eegprep.pac_cont(X, Y, srate, **kwargs)	Compute sliding-window phase-amplitude coupling from continuous data.
eegprep.timewarp(ev_latency, new_latency)	Return the linear interpolation matrix that warps event latencies.
eegprep.angtimewarp(ev_latency, new_latency, ...)	Warp an angular time series and wrap results to (-pi, pi].
eegprep.tf_cycle_calc([freqs, width, ...])	Calculate Morlet wavelet cycles from temporal or spectral width.
eegprep.newtimefbaseln(power, timesout, *[, ...])	Apply EEGLAB average baseline correction to absolute power.
eegprep.newtimeftrialbaseln(power, timesout, *)	Apply trial-level divisive or standardized baseline correction.
eegprep.newtimefitc(tfdecomp[, itctype])	Compute EEGLAB-style inter-trial coherence.
eegprep.newtimefpowerunit([params])	Return the display unit implied by newtimef baseline settings.
eegprep.bootstat(args, statistic, *[, ...])	Accumulate surrogate statistics and return EEGLAB-style thresholds.
eegprep.correct_mc(EEG[, cycles, freqrange, ...])	Estimate an EEGLAB-style multiple-comparison correction count.
eegprep.correctfit(pvalue, *[, allpval, ...])	Return a gamma-fit corrected p-value and fitted parameters.
eegprep.rsadjust(lambda3, lambda4, mean, ...)	Return lambda1 through lambda4 adjusted to mean and variance.
eegprep.rsfit(x, value[, plot, return_details])	Return a Ramberg-Schmeiser fitted p-value for value within x.
eegprep.rsget(lambdas, value)	Return the fitted Ramberg-Schmeiser cumulative probability at value.
eegprep.rspdfsolv(lambdas, skewness, kurtosis)	Return the Ramberg-Schmeiser moment residual for lambda3/lambda4.
eegprep.rspfunc(pvalue, lambdas, value)	Return the absolute quantile residual for one probability value.
eegprep.signalstat(data[, plotlab, dlabel, ...])	Compute EEGLAB-style summary statistics for a real-valued signal.
eegprep.pop_newtimef([EEG, typeproc, num, ...])	Plot a channel or component ERSP/ITC decomposition.
eegprep.pop_newcrossf([EEG, typeproc, num1, ...])	Plot event-related channel/component cross-coherence.
eegprep.pop_timef([EEG, typeproc, num, ...])	Run legacy pop_timef through EEGPrep's pop_newtimef implementation.
eegprep.pop_crossf([EEG, typeproc, num1, ...])	Run legacy pop_crossf through EEGPrep's pop_newcrossf implementation.
eegprep.pop_signalstat([EEG, typeproc, ...])	Compute and plot statistics for one channel or component.
eegprep.pop_eventstat([EEG, eventfield, ...])	Compute and plot statistics for numeric EEG event fields.

Epoching and Selection

eegprep.pop_adjustevents(EEG, *args[, ...])	Adjust event offset of all or selected events.
eegprep.pop_epoch([EEG, types, lim, gui, ...])	Extract data epochs time locked to event types or event indices.
eegprep.pop_select(EEG, *args[, gui, ...])	Select EEG data using EEGLAB pop_select semantics.
eegprep.eeg_eegrej(EEG, regions)	Reject EEG data segments specified by regions.
eegprep.eegrej(indata, regions, timelength)	Remove [beg end] sample ranges (1-based, inclusive) from continuous data and update events.

Visualization

eegprep.cart2topo(x, *args)	Convert XYZ Cartesian coordinates to polar topoplot coordinates.
eegprep.eegplot(data, *args, **kwargs)	Open an EEGLAB-style scrolling browser for channel-major EEG data.
eegprep.eeg_multieegplot(data[, rej, rejE, ...])	Open eegplot with current and previous rejection marks.
eegprep.pop_topoplot([EEG, typeplot, items, ...])	Plot EEGLAB-style 2-D scalp maps for ERP latencies or ICA components.
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, *checks[, load_data])	Validate and set up EEG dataset structure.
eegprep.eeg_compare(eeg1, eeg2[, ...])	Compare two EEG-like structures (or arrays) and return a difference summary.
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).

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_importbids(path, *[, return_com])	Load one or more EEG files from a BIDS dataset.
eegprep.pop_exportbids(EEG, output_dir, *[, ...])	Export EEG dataset(s) as BIDS-like EEGLAB files and sidecars.

Bundled Plugins

EEGPrep exposes metadata for bundled in-repo plugin ports. External EEGLAB plugin install/update/remove workflows are intentionally outside the public API for now.

eegprep.bundled_plugins()	Return metadata for EEGPrep extensions bundled in the installed package.
eegprep.plugin_status(pluginname, *[, ...])	Return EEGLAB-style installed status for EEGPrep extensions.
eegprep.plugin_menu([pluginlist, parent, ...])	Show or return the EEGPrep Extension Manager inventory.
eegprep.format_plugin_menu([pluginlist, ...])	Return a plain-text extension inventory for console display.
eegprep.pop_clean_rawdata(EEG, *args[, gui, ...])	Clean continuous EEG data using the clean_rawdata workflow.
eegprep.eeg_icalabelstat(EEG[, threshold, ...])	Return and optionally print ICLabel class statistics.
eegprep.pop_iclabel(EEG[, icversion, gui, ...])	Classify independent components using ICLabel.
eegprep.pop_icflag(EEG[, thresholds, gui, ...])	Flag ICLabel-classified components for later rejection.
eegprep.pop_prop_extended([EEG, typecomp, ...])	Render the EEGLAB viewprops-style extended property dashboard.
eegprep.pop_viewprops(EEG[, typecomp, ...])	Render channel/component property overview figures and activity views.
eegprep.firws(m, f[, t, w])	Designs windowed sinc type I linear phase FIR filter.
eegprep.firwsord(wintype, fs, df[, dev])	Estimate windowed sinc FIR filter order depending on window type and requested transition band width.
eegprep.pop_dipfit_settings([EEG, gui, ...])	Configure DIPFIT settings without requiring an EEGLAB runtime.
eegprep.pop_dipplot([EEG, comps, gui, ...])	Plot localized DIPFIT component positions.
eegprep.pop_dipfit_headmodel([EEG, mriFile, ...])	Validate headmodel inputs, then fail clearly until FieldTrip is ported.
eegprep.pop_dipfit_gridsearch([EEG, select, ...])	Run a standalone spherical coarse grid search for ICA components.
eegprep.pop_dipfit_nonlinear([EEG, ...])	Run standalone nonlinear dipole refinement for one component.
eegprep.pop_multifit([EEG, comps, gui, ...])	Fit multiple ICA components using grid search and nonlinear refinement.
eegprep.pop_leadfield([EEG, gui, renderer, ...])	Compute a standalone spherical leadfield for explicit source points.
eegprep.pop_dipfit_loreta([EEG, select, ...])	Validate LORETA prerequisites, then fail clearly until a backend exists.

Extension SDK

EEGPrep external extensions are Python packages discovered through the eegprep.extensions entry-point group. The registry validates declarative specs and keeps extension callables lazy until a later runtime surface uses them.

eegprep.CATALOG_SCHEMA_VERSION	int([x]) -> integer int(x, base=10) -> integer
eegprep.CatalogValidationIssue(message[, ...])	One catalog metadata validation issue.
eegprep.CatalogValidationOptions([...])	Validation switches for local and future catalog-CI checks.
eegprep.CatalogValidationReport([errors, ...])	Catalog validation result with blocking errors and non-blocking warnings.
eegprep.EXTENSION_COMPATIBILITY_POLICY	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
eegprep.EXTENSION_CURATION_POLICY_URL	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
eegprep.EXTENSION_NAMING_PREFIX	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
eegprep.EXTENSION_TRUST_MESSAGE	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
eegprep.ExtensionSpec(name[, display_name, ...])	Declarative metadata returned by an eegprep.extensions entry point.
eegprep.ExtensionRegistry(*[, ...])	Discover, validate, and report EEGPrep extensions.
eegprep.ExtensionRecord(name, status[, ...])	A discovered extension plus status and validation details.
eegprep.ExtensionStatus(value)	Registry status for an extension record.
eegprep.ExtensionSourceType(value)	Source category declared by an extension.
eegprep.ExtensionCatalog([entries, source, ...])	Loaded extension catalog plus source diagnostics.
eegprep.ExtensionCatalogEntry(name[, ...])	Curated metadata for an EEGPrep extension, without bundled code.
eegprep.CatalogSourceType(value)	Install/source categories accepted by the curated catalog.
eegprep.ExtensionAction(name, target[, ...])	Declarative action contributed by an extension.
eegprep.ExtensionPopFunction(name, target[, ...])	Declarative pop_* function contributed by an extension.
eegprep.ExtensionMenu([path, action, label, ...])	Declarative menu contribution for an extension action.
eegprep.ExtensionDependency(package[, ...])	Python distribution required by an extension.
eegprep.ExtensionResource(package, path)	Packaged resource declared by an extension.
eegprep.ExtensionLoadError	Raised when a lazily referenced extension object cannot be loaded.
eegprep.ExtensionValidationResult([...])	Validation errors grouped by registry status category.
eegprep.ExtensionTestHarness(spec)	Assertion helper for extension author test suites.
eegprep.LazyImport(module, attr)	Reference to an object that should be imported only when used.
eegprep.assert_extension_entry_point_loads(...)	Assert that an eegprep.extensions entry point loads to a usable spec.
eegprep.check_extension_compatibility(spec, *)	Check extension API, EEGPrep version, and dependency compatibility.
eegprep.discover_extensions(*[, ...])	Discover EEGPrep extensions with the default registry settings.
eegprep.extension_version_satisfies(version, ...)	Return whether version satisfies a simple PEP 440-style specifier.
eegprep.load_catalog_entries(path)	Load catalog entries from a JSON file or a directory of JSON files.
eegprep.validate_catalog_entries(entries, *)	Validate catalog metadata entries.
eegprep.validate_catalog_file(path, *[, options])	Validate catalog metadata loaded from path.
eegprep.validate_extension_spec(spec, *[, ...])	Validate an extension spec without importing its lazy action targets.
eegprep.load_extension_catalog([catalog_path])	Load the packaged or local metadata-only extension catalog.
eegprep.build_safe_install_commands(entry)	Return copyable install commands for a catalog entry without executing them.
eegprep.build_safe_update_commands(entry)	Return copyable update commands for a catalog entry without executing them.

STUDY Workflows

The STUDY wrappers and helpers below cover the integrated standalone STUDY workflow: metadata/design creation, study load/save, measure precompute, measure plotting, component preclustering, clustering, and cluster editing.

eegprep.pop_study([STUDY, ALLEEG, name, ...])	Create or edit a STUDY structure from loaded EEG datasets.
eegprep.pop_studywizard(filenames, *[, ...])	Load selected datasets and create a STUDY.
eegprep.pop_studyerp([ALLEEG, return_com])	Create a STUDY marked as a simple ERP design.
eegprep.pop_loadstudy([filename, filepath, ...])	Load a STUDY JSON file saved by pop_savestudy.
eegprep.pop_savestudy(STUDY[, EEG, ...])	Save a STUDY structure as an EEGPrep JSON .study file.
eegprep.pop_addindepvar(varlist[, fig, var, ...])	Return (variable, values, categorical_flag) for STUDY designs.
eegprep.pop_importgroupvar(STUDY[, ...])	Attach one imported variable value per STUDY subject.
eegprep.pop_listfactors(des, *args[, return_com])	Return factor descriptors for one STUDY or design structure.
eegprep.pop_precomp(STUDY, ALLEEG[, ...])	Precompute STUDY channel or component measures.
eegprep.pop_chanplot([STUDY, ALLEEG, ...])	Plot precomputed STUDY channel or component measures.
eegprep.pop_preclust(STUDY, ALLEEG[, ...])	Prepare a STUDY component clustering matrix.
eegprep.pop_clust(STUDY, ALLEEG, *args[, ...])	Run deterministic k-means clustering on STUDY.etc.preclust.
eegprep.pop_clustedit(STUDY, ALLEEG[, ...])	Run cluster edit and plotting actions on STUDY.cluster.
eegprep.std_addvarlevel(STUDY[, designind])	Return STUDY with one/two design-variable levels.
eegprep.std_builddesignmat(design, trialinfo)	Build a design matrix from a STUDY design and trial metadata.
eegprep.std_limodesign(factors, trialinfo, *args)	Create LIMO-compatible categorical and continuous design matrices.
eegprep.std_checkconsist(STUDY, *args[, ...])	Check whether each value of a STUDY factor has the same subject count.
eegprep.std_checkdesign(STUDY[, designind])	Return 1 when a design has no continuous variables or multi-valued extras.
eegprep.std_combtrialinfo(datasetinfo, inds)	Return trial rows enriched with selected datasetinfo fields.
eegprep.std_findsameica(ALLEEG[, icathreshold])	Group 1-based dataset indices with near-identical ICA weights*sphere.
eegprep.std_getindvar(STUDY[, mode, scandesign])	Return STUDY factor names, values, subject groupings, and pairing flags.
eegprep.std_gettrialsind(trialinfo, *args[, ...])	Return 1-based trial indices matching all requested trialinfo values.
eegprep.std_indvarmatch(value, valuelist)	Return 1-based indices where an independent-variable value matches.
eegprep.std_maketrialinfo(STUDY, ALLEEG)	Populate STUDY.datasetinfo[*].trialinfo from loaded EEG metadata.
eegprep.std_rmdat(STUDY, ALLEEG, *args[, ...])	Remove datasets from STUDY/ALLEEG and return removed 1-based indices.
eegprep.std_rmalldatafields(STUDY[, chanorcomp])	Return STUDY without cached measure/data fields for the requested target.
eegprep.std_rebuilddesign(STUDY[, ALLEEG, ...])	Refresh STUDY design variables after dataset metadata changes.
eegprep.std_saveindvar(STUDY, *[, return_com])	Save factor descriptors under STUDY.etc.eegprep.independent_variables.
eegprep.std_selectdataset(STUDY, ALLEEG[, ...])	Return selected 1-based dataset indices and trial indices.
eegprep.std_selsubject(data, subject, ...[, ...])	Return cached-measure cells with only the requested subject columns.
eegprep.std_substudy(STUDY, ALLEEG, *args[, ...])	Return a STUDY/ALLEEG subset using EEGLAB-facing 1-based selectors.
eegprep.std_readdata(STUDY[, ALLEEG, ...])	Read precomputed STUDY measures from EEGPrep's in-memory cache.
eegprep.std_readerp(STUDY[, ALLEEG])	Read cached STUDY ERP measures.
eegprep.std_readspec(STUDY[, ALLEEG])	Read cached STUDY spectrum measures.
eegprep.std_readersp(STUDY[, ALLEEG])	Read cached STUDY ERSP measures.
eegprep.std_readitc(STUDY[, ALLEEG])	Read cached STUDY ITC measures.
eegprep.std_readtopo(STUDY[, ALLEEG, ...])	Read cached STUDY component scalp topographies.
eegprep.std_readpac(STUDY[, ALLEEG, ...])	Read EEGPrep-owned cached STUDY PAC data when present.
eegprep.std_pac(EEG_or_STUDY[, ALLEEG, ...])	Compute phase-amplitude coupling for an EEG dataset or STUDY.
eegprep.std_pacplot(STUDY, ALLEEG, *args[, ...])	Read and optionally plot precomputed STUDY PAC measures.
eegprep.std_prepare_neighbors(STUDY, ALLEEG, ...)	Prepare a FieldTrip-like neighbor list and LIMO adjacency matrix.
eegprep.std_interp(STUDY, ALLEEG[, chans, ...])	Interpolate selected missing channels into every STUDY dataset.
eegprep.std_dipplot(*args, **kwargs)	Report the standalone boundary for STUDY-level source plotting.
eegprep.std_dipoleclusters(*args, **kwargs)	Report the standalone boundary for STUDY dipole-cluster workflows.
eegprep.std_checkfiles(STUDY[, ALLEEG, ...])	Check loaded STUDY data and cached measures for standalone EEGPrep.
eegprep.std_checkdatasession(STUDY[, ...])	Check dataset/session alignment for a STUDY and loaded ALLEEG.
eegprep.std_uniformfiles(STUDY, ALLEEG)	Return 1 for uniform channels, 0 for mismatch, -1 for missing files.
eegprep.std_uniformsetinds(STUDY)	Return 1 when STUDY channel group set indices are uniform.
eegprep.std_erpplot(STUDY, ALLEEG, *args, ...)	Read and plot precomputed STUDY ERP measures.
eegprep.std_specplot(STUDY, ALLEEG, *args, ...)	Read and plot precomputed STUDY spectrum measures.
eegprep.std_erspplot(STUDY, ALLEEG, *args, ...)	Read and plot precomputed STUDY ERSP measures.
eegprep.std_itcplot(STUDY, ALLEEG, *args, ...)	Read and plot precomputed STUDY ITC measures.
eegprep.optimal_kmeans(clustdata, clusnum, *)	Run k-means for a range and choose the best silhouette score.
eegprep.robust_kmeans(data, clus_num[, STD, ...])	Cluster rows and iteratively mark distant rows as outliers.
eegprep.std_apcluster(clustdata, *[, ...])	Cluster rows using deterministic affinity propagation updates.
eegprep.std_centroid(data[, labels])	Return centroids for all rows or for each positive label.
eegprep.std_findoutlierclust(data[, labels, ...])	Return 1-based row indices farther than threshold cluster spreads.

Configuration

• eegprep.EEG_OPTIONS