meeg-utils Documentation

MEG and EEG quality control, preprocessing and epoching on top of MNE-Python: scikit-learn style steps with documented defaults, QC metrics and figures for every step, BIDS in and out.

import meeg_utils as meu

raw = meu.io.read("bids/sub-01/eeg/sub-01_task-oddball_eeg.vhdr")
meu.qc.inspect(raw)                     # how well was it recorded?
pipe = meu.Pipeline.preset("eeg-erp")   # every parameter has a cited source
clean = pipe.fit_transform(raw)
pipe.plot(inst=raw)                     # figures of every step

New here? Start with the Tutorials: they run on real data and show what every step does.

• Tutorials: Quickstart: from a raw recording to clean epochs, Acquisition quality control, Build and adjust a pipeline, Epochs, artifact rejection and combining runs, Whole BIDS datasets

• User guide: Installation, Steps and pipelines, Quality control, Presets, Batch processing, Command line

• API Reference: every function and parameter

• Changelog

Analysis (ERP/ERF measures, spectra, time-frequency, group statistics) is planned; the outputs are plain MNE objects, so MNE’s own analysis tools apply directly.