Time-Frequency

Time-frequency decomposition, coherence, phase-amplitude coupling, and time warping.

eegprep.angtimewarp(ev_latency, new_latency, ...)	Warp an angular time series and wrap results to (-pi, pi].
eegprep.bootstat(args, statistic, *[, ...])	Accumulate surrogate statistics and return EEGLAB-style thresholds.
eegprep.crossf(x, y, frames, tlimits, srate)	Compute legacy crossf outputs using EEGPrep's newcrossf core.
eegprep.newcrossf(x, y, frames, tlimits, srate)	Compute an EEGLAB-like event-related coherence decomposition.
eegprep.newtimef(data, frames, tlimits, srate)	Compute an EEGLAB-like ERSP/ITC time-frequency decomposition.
eegprep.newtimefbaseln(power, timesout, *[, ...])	Apply EEGLAB average baseline correction to absolute power.
eegprep.newtimefitc(tfdecomp[, itctype])	Compute EEGLAB-style inter-trial coherence.
eegprep.newtimefpowerunit([params])	Return the display unit implied by newtimef baseline settings.
eegprep.newtimeftrialbaseln(power, timesout, *)	Apply trial-level divisive or standardized baseline correction.
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.tf_cycle_calc([freqs, width, ...])	Calculate Morlet wavelet cycles from temporal or spectral width.
eegprep.timef(data, frames, tlimits, srate)	Compute legacy timef outputs using EEGPrep's newtimef core.
eegprep.timefreq(data, srate, *[, frames, ...])	Return EEGLAB-like spectral estimates for each trial.
eegprep.timewarp(ev_latency, new_latency)	Return the linear interpolation matrix that warps event latencies.