mne-python

# 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 ```bash pip install mne # optional but commonly needed for decoding / ICA workflows pip install scikit-learn ``` --- ## Data Containers and I/O ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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+) ```bash 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.