MNE-Python

MNE-Python is a scientific Python toolkit for electrophysiology analysis (MEG, EEG, iEEG, fNIRS), covering data structures, preprocessing, spectral analysis, source modeling, and visualization.

Version

Built against: mne==1.11.0 Python: 3.13.5

If using a different version, check assets/version.txt and consult doc/changes/v1.11.rst for API differences.

Scope

This skill is intentionally scoped to common workflows for day-to-day analysis:

Included: mne.io, mne.epochs, mne.preprocessing, mne.time_frequency, mne.viz, mne.report, basic forward/inverse setup.
Out of scope: exhaustive coverage of all format-specific readers, every dataset helper, all advanced beamformer/minimum-norm variants, and low-level internals under mne._fiff / mne.commands.

Coverage Profile

Coverage profile is hybrid:

Workflow coverage: curated day-to-day analysis paths in references/*.md.
Dictionary coverage: broad symbol lookup assets for API discovery without opening source files first.

Dictionary assets:

assets/symbol-index.md
assets/symbol-index.jsonl
assets/symbol-cards/

Installation

pip install mne

# optional but commonly needed for decoding / ICA workflows
pip install scikit-learn

Data Containers and I/O

import numpy as np
import mne

# tested against mne==1.11.0
info = mne.create_info(["Fz", "Cz"], sfreq=100.0, ch_types="eeg")
raw = mne.io.RawArray(np.random.randn(2, 300), info)
print(raw.get_data().shape)

See references/data-containers-and-io.md for signatures, parameter details, and shape conventions.

Events, Epochs, and Evoked

import numpy as np
import mne

# tested against mne==1.11.0
info = mne.create_info(["EEG 001"], sfreq=100.0, ch_types="eeg")
raw = mne.io.RawArray(np.random.randn(1, 1000), info)
events = mne.make_fixed_length_events(raw, duration=0.5)
epochs = mne.Epochs(raw, events, event_id={"stim": 1}, tmin=0.0, tmax=0.2, baseline=None, preload=True, verbose=False)
print(len(epochs))

See references/events-epochs-and-evoked.md for event construction and epoching pitfalls.

Preprocessing and Artifacts

import mne

# tested against mne==1.11.0
ica = mne.preprocessing.ICA(n_components=2, method="fastica", random_state=0)
print(ica.method)

See references/preprocessing-and-artifacts.md for covariance estimation and ICA usage notes.

Spectral and Time-Frequency

import numpy as np
import mne

# tested against mne==1.11.0
sfreq = 200.0
t = np.arange(0, 2, 1 / sfreq)
signal = np.sin(2 * np.pi * 10 * t)
psd, freqs = mne.time_frequency.psd_array_welch(signal, sfreq=sfreq, fmin=1, fmax=40)
print(psd.shape[0], round(float(freqs[psd.argmax()]), 1))

See references/spectral-and-time-frequency.md for Welch parameters and PSD workflow patterns.

Forward, Inverse, and Source Basics

import mne

# tested against mne==1.11.0
sphere = mne.make_sphere_model(head_radius=0.09)
print(type(sphere).__name__)

See references/forward-inverse-and-source.md for sphere/BEM context and covariance setup.

Visualization and Reporting

import mne

# tested against mne==1.11.0
report = mne.Report(title="Synthetic QC")
report.add_html("<p>ok</p>", title="status")
print(len(report))

See references/visualization-and-reporting.md for report and plotting patterns.

Verification (Medium+)

python .opencode/skills/opensci-skill/scripts/verify-snippets.py --root .opencode/skills/mne-python --fail-fast

API Dictionary (Dictionary/Hybrid)

assets/symbol-index.md - Module-level symbol navigation for quick browsing.
assets/symbol-index.jsonl - Machine-readable symbol lookup records.
assets/symbol-cards/ - Per-module symbol cards with signatures and anchors.

Quick Reference

Function / Class	Purpose
`mne.create_info()`	Build channel metadata used by all core containers.
`mne.io.RawArray()`	Wrap in-memory continuous data as Raw.
`mne.make_fixed_length_events()`	Create regular event markers for windowing.
`mne.Epochs()`	Segment continuous raw data into event-locked trials.
`mne.compute_covariance()`	Estimate noise/data covariance from epochs.
`mne.preprocessing.ICA()`	Configure ICA decomposition for artifact handling.
`mne.time_frequency.psd_array_welch()`	Compute PSD from arrays with Welch method.
`mne.make_sphere_model()`	Build simple head conductivity model.
`mne.Report()`	Build HTML QC/report artifacts.

Module Map

Submodule	Contents	Notes
`mne.io`	Raw classes and format readers/writers	Core ingest path
`mne.preprocessing`	Artifact detection/correction, ICA, filtering helpers	High-use preprocessing APIs
`mne.time_frequency`	PSD/TFR/CSD estimators	Spectral workflows
`mne.viz`	Sensor/source plotting backends	Large surface area
`mne._fiff.meas_info`	Metadata internals (`Info`)	`[LARGE]` (>500 lines)

Import style: lazy __getattr__ + stub-driven __all__ via lazy_loader.attach_stub().

See assets/module-map.md for complete submodule inventory and large-module flags.

References

references/data-containers-and-io.md - Metadata creation, in-memory Raw objects, and shape contracts.
references/events-epochs-and-evoked.md - Event generation, trial segmentation, and evoked averaging basics.
references/preprocessing-and-artifacts.md - Covariance estimation and ICA setup for artifact workflows.
references/spectral-and-time-frequency.md - Welch PSD and common spectral workflow patterns.
references/forward-inverse-and-source.md - Sphere models, ad-hoc covariance, and source pipeline entry points.
references/visualization-and-reporting.md - Report generation and quick visualization artifacts.