Signal Processing Functions#

This section documents the signal processing functions for spectral analysis, resampling, and baseline removal.

Spectral Analysis#

eegprep.eeg_autocorr(EEG, pct_data=None)#

Compute autocorrelation of ICA components.

eegprep.eeg_autocorr_welch(EEG, pct_data=100)#

Compute autocorrelation of EEG ICA components using Welch method.

EEGdict: EEG data structure with ‘icaweights’, ‘icaact’, ‘pnts’, ‘srate’ fields.
pct_datafloat, optional: Percentage of data to use. Default 100.

eegprep.eeg_rpsd(EEG, nfreqs=None, pct_data=100)#

Compute relative power spectral density for ICA components.

EEGdict: EEG data structure with ICA activations.
nfreqsint, optional: Number of frequency bins. Default is Nyquist frequency.
pct_datafloat, optional: Percentage of data to use. Default is 100.

eegprep.pop_resample(EEG, freq, engine=None)#

Resample EEG data to a new sampling rate.

EEGdict: EEGLAB EEG structure.
freqfloat: New sampling rate in Hz.
enginestr or None: Engine to use for implementation. Options are: - None: Use the default Python implementation - ‘poly’: Use scipy’s resample_poly function - ‘matlab’: Use MATLAB engine - ‘octave’: Use Octave engine

eegprep.pop_rmbase(EEG, timerange=None, pointrange=None, chanlist=None)#

POP_RMBASE - remove channel baseline means from an epoched or continuous EEG dataset.

Return type:

dict

Parameters:

EEGdict: EEGLAB-like EEG structure with keys: ‘data’, ‘nbchan’, ‘pnts’, ‘trials’, ‘times’, ‘event’. Event latencies are 1-based indices (EEGLAB convention).
timerange[min_ms, max_ms] or None: Baseline latency range in milliseconds; overrides pointrange when provided.
pointrangeiterable of indices or [start, end]: Baseline sample indices (0-based or 1-based tolerated). If None/empty, use whole epoch.

chanlist : iterable of channel indices (0-based). If None, all channels are used.

EEGdict: Updated EEG structure with baseline removed. EEG[‘icaact’] is cleared.