API Reference

Pipelines and steps

Pipeline(steps, *[, copy])	A sequence of named processing steps.
Step()	Base class for all processing steps.
process(pipeline, sources, root, *[, desc, ...])	Process each recording with its own copy of a pipeline and save the results.
Dataset(root, *[, datatype, subjects, ...])	The MEG/EEG recordings of a BIDS dataset, selected by entity.
process_dataset(dataset, root, *, preprocessing)	Process a whole dataset: each run, then the epochs of each session.
BatchResult(records)	Outcome of process_dataset(): one record per run and stage, and per group.

Processing steps (meeg_utils.steps)

steps.Filter(l_freq, h_freq, *[, picks, ...])	Band-pass, high-pass or low-pass filter the data.
steps.Resample(sfreq, *[, npad, window, ...])	Resample the data.
steps.LineNoise([method, fline, n_remove, ...])	Remove power-line noise.
steps.BadChannels([method, ransac, psd, ...])	Detect bad channels and mark them in info["bads"].
steps.Interpolate([method, origin, ...])	Interpolate the channels marked bad.
steps.Reference([eeg, projection, ctf_grade])	Set the EEG reference and, for CTF MEG, the gradient compensation grade.
steps.ICA([n_components, picks, method, ...])	Decompose the data with ICA, label components and remove artifacts.
steps.HeadAlign(destination, *[, origin, mode])	Map MEG data to a common head position (device-to-head transform).
steps.Maxwell(*[, st_duration, ...])	Signal space separation (SSS) and its temporal extension (tSSS).
steps.ByChannelType(branches, *[, on_missing])	Process channel types with separate steps, like scikit-learn's ColumnTransformer.
steps.HFC([order, accuracy])	Remove environmental fields from OPM data by a homogeneous or harmonic field model.
steps.Regression([artifact, picks])	Remove what reference or artifact channels record from the data, by linear regression.
steps.BadSegments([methods, reject, flat, ...])	Mark bad stretches of a recording with BAD_* annotations.
steps.ASR([cutoff, picks, window_length, ...])	Artifact subspace reconstruction: repair high-amplitude bursts in continuous EEG.
steps.SNS([n_neighbors, skip, n_iter])	Sensor noise suppression: replace each channel by its projection on its neighbours.
steps.BridgedElectrodes(*[, lm_cutoff, ...])	Detect bridged EEG electrodes and interpolate them.
steps.DropChannels(ch_names, *[, on_missing])	Remove channels, e.g. mastoid electrodes before re-referencing.
steps.Epoch([event_id, tmin, tmax, ...])	Cut the recording into epochs around events.
steps.FixedLengthEpochs([duration, overlap, ...])	Cut a continuous recording into consecutive epochs of equal length (e.g. resting state).
steps.Baseline([baseline, mode, scale, ...])	Baseline correction of epochs or evoked data.
steps.AutoReject([method, n_interpolate, ...])	Reject or repair bad epochs with thresholds learned from the data.
epochs.combine(epochs_list, *[, bads, ...])	Concatenate the epochs of several runs of one session.

Reading and writing (meeg_utils.io)

io.read(path, *[, preload])	Read a recording into memory, using BIDS metadata when available.
io.save_derivative(inst, source, root, *[, ...])	Save processed data as a BIDS derivative, with a provenance sidecar.
io.existing_derivatives(source, root, *[, desc])	Return the derivative files already written for source, if any.
io.detect_system(inst)	Detect the acquisition system of a recording.
io.get_datatypes(inst)	Return the modalities ("meg" and/or "eeg") present in a recording.
io.average_dev_head_t(sources)	Average device-to-head transform of several MEG recordings.

Quality control (meeg_utils.qc)

qc.inspect(raw[, checks])	Run acquisition-quality checks on a recording.
qc.inspect_dataset(sources[, checks, ...])	Run the quality checks on every recording of a dataset.
qc.find_recordings(root)	Return every MEG and EEG recording of a BIDS dataset.
qc.QCReport(checks, skipped[, source])	The results of several checks on one recording.
qc.DatasetQC(reports, errors, *[, ...])	Quality-check results of many recordings.
qc.Finding(check, metric, value, level, message)	One evaluated quality criterion.
qc.Check()	Base class of acquisition-quality checks.
qc.Amplitude(*[, min_flat_duration, ...])	Flat signals, clipping (amplifier saturation) and missing samples.
qc.Bridging(*[, lm_cutoff, epoch_threshold, ...])	Electrode bridging: neighbouring electrodes shorted by gel or sweat.
qc.Impedance(*[, impedances, warn_kohm])	Electrode impedances measured before or during the recording.
qc.OutlierChannels(*[, n_neighbors, ...])	Channels whose signal is unlike that of the other channels (LOF).
qc.NarrowbandNoise(*[, fline, fmin, fmax, ...])	Power-line noise and other narrowband noise in the raw spectrum.
qc.Muscle(*[, threshold, filter_freq, ...])	Share of the recording contaminated by muscle activity.
qc.Blinks(*[, min_rate])	Blink rate from the EOG channel(s), a test that the EOG works.
qc.HeartRate(*[, min_bpm, max_bpm, ...])	Heart rate from the ECG channel (or MEG magnetometers), a test of the ECG.
qc.HeadMovement(*[, head_pos, warn_mm, ...])	Head movement during an MEG recording, from continuous head localization.
qc.Digitization()	Electrode positions, head digitization and head-to-sensor distance.
qc.Events(*[, expected, stim_channel, ...])	Event counts and timing, compared with what the experiment should produce.
qc.SquidJumps(*[, cutoff, min_shift])	Flux jumps of SQUID sensors: sudden, lasting steps in an MEG channel.
qc.ChpiSNR(*[, max_drop_db])	Signal-to-noise ratio of the continuous head-position (cHPI) coils.
qc.EmptyRoom(*[, empty_room, band, max_days])	The empty-room recording of the session: sensor noise and noisy sensors.
qc.BidsMetadata()	Whether the BIDS sidecar describes the recording correctly.
qc.Photodiode(channel, *[, events, ...])	Delay and jitter between the triggers and what the screen showed.
report.build([pipeline, inst, qc, title])	Build an mne.Report with every metric and figure.
report.from_derivative(fname, *[, title])	Build a report of a derivative saved by meeg_utils.io.save_derivative().
report.summarize(root, *[, desc, min_files])	Collect the QC metrics of every derivative under root.
report.DerivativesSummary(table, warnings_, *)	QC metrics of every derivative in a folder, with outlying files flagged.

Presets (meeg_utils.presets)

presets.available()	Return the preset names with the first line of their description.
presets.get(name, **kwargs)	Build the pipeline of a preset.
presets.options(name)	Return the options a preset accepts.

Logging and testing

setup_logging([level, log_file, file_level, ...])	Show meeg-utils log messages on the console and/or in a file.
teardown_logging()	Remove the handlers added by setup_logging() and silence meeg-utils again.
log_to_file(log_file[, level])	Write meeg-utils log messages to a file for the duration of a with block.
testing.check_step(step, inst)	Check that a step honours the meeg-utils step contract.
testing.check_check(check, raw)	Check that a quality check honours the meeg-utils check contract.