Preprocessing and Artifacts

Artifact detection and repair, referencing, resampling, interpolation, and rejection.

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_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.clean_drifts(EEG[, transition, ...])	Remove drifts from the data using a forward-backward high-pass filter.
eegprep.clean_flatlines(EEG[, ...])	Remove (near-) flat-lined channels.
eegprep.clean_rawdata_vis_artifacts(clean_eeg)	Compare a cleaned dataset against its original clean_rawdata source.
eegprep.clean_rawdata_vis_artifacts_diagnostics(...)	Return clean_rawdata sample/channel rejection diagnostics.
eegprep.clean_windows(EEG[, ...])	Remove periods with abnormally high-power content from continuous data.
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.eeg_eegrej(EEG, regions)	Reject EEG data segments specified by regions.
eegprep.eeg_interp(EEG, bad_chans[, method, ...])	Interpolate missing or bad EEG channels using spherical spline.
eegprep.eeg_rejsuperpose(EEG, typerej, ...)	Combine stored rejection marks into EEG.reject.rejglobal.
eegprep.eeglab_clean_artifacts(EEG[, ...])	Clean artifacts from EEG data using EEGLAB's clean_artifacts.
eegprep.eegrej(indata, regions, timelength)	Remove [beg end] sample ranges (1-based, inclusive) from continuous data and update events.
eegprep.eegthresh(signal, pnts, electrodes, ...)	Reject epochs with out-of-bounds values in a time window.
eegprep.entropy_rej(signal[, threshold, ...])	Return entropy scores and rejection flags along the trial dimension.
eegprep.interp(*args, **kwargs)	Wrap eeg_interp.
eegprep.jointprob(signal[, threshold, ...])	Port of EEGLAB jointprob for deterministic rejection scoring.
eegprep.kurt(data)	Return column-wise excess kurtosis using EEGLAB's legacy formula.
eegprep.realproba(data[, bins])	Compute per-sample empirical probabilities and the distribution.
eegprep.rejkurt(signal[, threshold, ...])	Port of EEGLAB rejkurt for deterministic rejection scoring.
eegprep.rejtrend(signal, pointrange, ...[, step])	Detect trials containing linear trends with high slope and R-squared.
eegprep.reref(*args, **kwargs)	Wrap pop_reref.
eegprep.resample(*args, **kwargs)	Wrap pop_resample.
eegprep.rmbase(data[, frames, basevector, ...])	Subtract per-channel baseline means from continuous or epoched data.
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.