MNE-MCP

Group-level sign-flip inference on mne_decode mean scores, one per independent subject. Requires score_names, unique subject_ids, independent_subjects=true and explicit null_value. Never pass CV folds or repeated runs as subjects. Supports ROC AUC/balanced accuracy, matching time grids and methods. max_t: two-sided pointwise FWER across the whole curve/matrix; cluster: cluster-mass FWER with time or train-time/test-time lattice adjacency. Requires symmetric subject effects under the null. Not single-subject label shuffling or population prevalence. Stores statistic, corrected p values or clust…

Show a detailed summary of one named session object (channels, sfreq, montage, bads, etc.).

Convert a Raw object's annotations into an events array + event_id map (for EDF/BrainVision/EEGLAB data).

Band-pass / high-pass / low-pass and/or notch filter a Raw/Epochs/Evoked object in place. l_freq=high-pass edge, h_freq=low-pass edge (either may be null), notch=line-noise frequency (e.g. 50 or 60). picks optional ('eeg', 'meg', or null).

Find stimulus/trigger events on a stim channel of a Raw object. Stores them under events_name.

Fit Independent Component Analysis on a (preferably 1 Hz high-pass filtered) Raw/Epochs object for artifact removal. n_components can be an int, a float (variance fraction), or null. method: 'fastica' (default), 'infomax', 'picard'. Stored under ica_name (default 'ica'). Requires scikit-learn.

Show the configured default analysis parameters (line frequency, default montage, filter band, rejection threshold, ICA method/components, epoch window, dirs, timeout) that the structured tools fall back to when a parameter is omitted. Users change these by running `mne-mcp configure` in a terminal.

Show the full channel list and measurement info for a named session object.

Interpolate currently-marked bad channels using spherical splines (requires a montage).

List neurophysiology data files (.fif, .edf, .bdf, .vhdr, .set, .cnt, .egi/.mff, .ds, .snirf, …) under a directory. Defaults to MNE_MCP_DATA_DIR / current dir. Optionally pass a glob pattern.

Load a raw recording from disk into the session. Auto-detects the format by extension (FIF/EDF/BDF/BrainVision/EEGLAB/CNT/EGI/…). Stores it under `name` (default `raw`). Set preload=False for very large files.

Segment a Raw object into Epochs around events. tmin/tmax in seconds relative to the event; baseline 'default' = (None, 0); event_id like 'target:1,standard:2' to name/select conditions; reject_eeg = peak-to-peak EEG rejection threshold in volts (e.g. 100e-6). Prefer JSON event_id={label: code} and baseline=[start, end] or null; legacy strings remain supported. reject/flat map channel types to SI thresholds; reject={} disables configured rejection. Do not combine reject with reject_eeg. Stored under epochs_name.

Build a template-head (fsaverage) EEG forward model for the named object's montage. Downloads the fsaverage template once (~ tens of MB). Use for EEG source localization without an individual MRI. Stored under fwd_name.

Mark channels as bad (comma-separated names, e.g. 'Fp1,T7'). By default appends to existing bads; set replace=true to overwrite.

Plot an ERP image (epochs × time heatmap) for an Epochs object. Returns PNG path(s).

Plot an Evoked response. style: 'joint' (butterfly + topomaps, default), 'topo', or 'butterfly'. Returns PNG path.

Plot ICA component scalp topographies (to identify eye/heart/muscle artifacts). Returns PNG path(s).

Plot ICA component time courses for an instrument (raw/epochs). Returns a PNG path.

Plot the power spectral density of a Raw/Epochs/Evoked object. Returns a PNG path.

Plot raw signal traces (a window of channels over time). Returns a PNG path.

Plot the sensor/electrode layout (kind='topomap' 2D or '3d'). Returns a PNG path.

Render a source estimate (stc) as a cortical activation map (PNG) at its peak time or a given time. hemi: 'both' / 'lh' / 'rh'. Requires PyVista with off-screen rendering; if 3D rendering is unavailable the estimate is still computed and can be inspected via mne_run_code.

Plot scalp topographies of an Evoked at given times. times='auto', 'peaks', or comma-separated seconds (e.g. '0.1,0.2,0.3'). Returns PNG path.

Resample a Raw/Epochs object to a new sampling frequency (Hz), in place.

Clear all loaded objects and figures from the session, starting fresh. Irreversible.

Execute arbitrary Python/MNE code in the persistent session namespace. Pre-bound names: `mne`, `np`, `pd`, `plt`, plus every object you have loaded (e.g. `raw`, `epochs`, `evoked`, `ica`). Like a notebook cell: the value of a final expression is returned, stdout is captured, and any matplotlib figures are saved as PNG (paths returned). Use this for anything the structured tools do not cover.

Save a session object to disk. MNE naming rules: Raw → '*_raw.fif', Epochs → '*-epo.fif', Evoked → '*-ave.fif'. Other formats follow the object's .save() support.

List every object currently held in the persistent analysis session (raw recordings, epochs, evoked, ICA, events, arrays) with a one-line summary. Use this to see what is loaded before operating on it.

Apply a standard electrode montage (e.g. 'standard_1020', 'standard_1005', 'biosemi64', 'GSN-HydroCel-128') to set channel positions. Needed before topographic plots and interpolation. If montage is omitted, uses the configured default (set via `mne-mcp configure`).

Set the EEG reference. Use 'average' for average reference, 'REST', or a comma-separated list of channel names (e.g. 'TP9,TP10').

Compute Morlet-wavelet time-frequency power on Epochs and plot it. fmin/fmax = frequency range (Hz), n_freqs = number of frequencies. Stored under tfr_name. Returns PNG path.