ICA and Components

Decomposition, component classification, and component measures.

eegprep.ICL_feature_extractor(EEG[, ...])	Extract features for ICLabel classification.
eegprep.compvar(data, act, winv, compnums)	Back-project components and compute percent variance accounted for.
eegprep.eeg_amica(EEG[, posact, sortcomps, ...])	Perform ICA decomposition using AMICA (Adaptive Mixture ICA).
eegprep.eeg_autocorr(EEG[, pct_data])	Compute autocorrelation of ICA components.
eegprep.eeg_autocorr_fftw(EEG[, pct_data])	Compute autocorrelation of EEG ICA components using FFT.
eegprep.eeg_autocorr_welch(EEG[, pct_data])	Compute autocorrelation of EEG ICA components using Welch method.
eegprep.eeg_getica(EEG[, comp])	Return stored or recomputed ICA component activations.
eegprep.eeg_icalabelstat(EEG[, threshold, ...])	Return and optionally print ICLabel class statistics.
eegprep.eeg_icflag(EEG, thresholds)	Flag independent components based on ICLabel classification probabilities.
eegprep.eeg_picard(EEG[, engine, posact, ...])	Perform ICA decomposition using Picard algorithm.
eegprep.eeg_pv(EEG[, comps, artcomps, ...])	Compatibility alias for eeg_pvaf using legacy positional options.
eegprep.eeg_pvaf(EEG[, comps, artcomps, ...])	Compute percent variance accounted for by ICA component projections.
eegprep.eeg_rpsd(EEG[, nfreqs, pct_data])	Compute relative power spectral density for ICA components.
eegprep.eeg_runica(EEG[, posact, sortcomps])	Perform ICA decomposition using runica (infomax) algorithm.
eegprep.icaact(data, weights[, datamean])	Compute ICA activation waveforms from channel data and weights.
eegprep.icaproj(data, weights, compindex[, ...])	Back-project selected ICA components into channel space.
eegprep.icavar(data, weights[, sphere, compnums])	Return per-frame scalp variance contributed by selected components.
eegprep.iclabel(EEG[, algorithm, engine])	Apply ICLabel to classify independent components.
eegprep.optimal_kmeans(clustdata, clusnum, *)	Run k-means for a range and choose the best silhouette score.
eegprep.picard(*args, **kwargs)	Wrap eeg_picard.
eegprep.robust_kmeans(data, clus_num[, STD, ...])	Cluster rows and iteratively mark distant rows as outliers.