| name | mne-python |
| description | MNE-Python for MEG/EEG/iEEG/fNIRS analysis pipelines, from loading raw signals to epoching, preprocessing, time-frequency analysis, inverse modeling, and reporting. Use when working with mne.create_info, mne.io.RawArray, mne.Epochs, mne.compute_covariance, mne.preprocessing.ICA, mne.time_frequency.psd_array_welch, mne.make_sphere_model, mne.Report, sensor/source-space workflows, or electrophysiology quality control. |
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.mdassets/symbol-index.jsonlassets/symbol-cards/
Installation
pip install mne
pip install scikit-learn
Data Containers and I/O
import numpy as np
import mne
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
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
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
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
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
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.
