Check MNE MCP capabilities: MNE-Python version, scikit-learn (needed for ICA), numpy/scipy/matplotlib versions, and runtime directories. Call this first.

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.

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.

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

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

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.

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.

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).

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

Crop a Raw/Epochs/Evoked object to the time window [tmin, tmax] seconds, in place.

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').

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

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

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.

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.

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.

Remove ICA components from an instrument in place. exclude = comma-separated component indices to drop (e.g. '0,3'); if omitted, uses the ICA object's current exclude list.

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

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

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.

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

Average Epochs into an Evoked (ERP/ERF) response. condition = an event_id name to average just that condition (else averages all). Stored under evoked_name.

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

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.

Compute Morlet or multitaper Epochs power with explicit frequencies, scalar/per-frequency n_cycles, channel picks, decimation, trial retention, optional ITC and power baseline normalization. Pass a params JSON object. Averaged trial power is total power, not strictly induced power. ITC requires average=true. Returns output names, shape, optional figure paths and reproducible code. Input epochs are unchanged.

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.

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.

Time-resolved decoding (MVPA): train a classifier at each time point to discriminate two conditions, with cross-validation. cond_a/cond_b are event_id names (e.g. 'target','standard'). Supports stratified, stratified_group or leave_one_group_out CV. groups must align with ALL retained input epochs before condition filtering. Saves mean scores under name, per-fold scores under name_folds and split diagnostics under name_details. method='sliding' returns (time,), 'generalizing' returns (train_time, test_time). Optional tmin/tmax crop a copy in seconds. C>0, class_weight=null|'balanced' and max_iter configure fold-local logistic regression. Choose them before CV or use nested CV via mne_run_code for tuning. Reference lines are not significance. Requires scikit-learn.

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 cluster p values, mask, H0 and diagnostics.

Spectral connectivity between channels over Epochs in a frequency band. method: 'coh', 'plv', 'wpli', 'pli', 'imcoh', etc. Returns an ordered-edge heatmap without forcing symmetry. Use mne_compute_connectivity for multi-band, channel-pair and estimator parameters. Requires mne-connectivity.

Bivariate across-trial connectivity with a params JSON object: multiple bands, ordered channel-name pairs, picks, epoch-relative time window, multitaper/fourier/cwt_morlet estimation and smoothing/cycles. Preserves signed, directed and complex values. Stores an MNE Connectivity object; optional first-30-edge heatmap (CWT time mean, complex magnitude only for display). Requires mne-connectivity. Not Granger/PAC.

Compute a noise covariance matrix from the Epochs baseline (data up to tmax seconds, default 0). Needed before building an inverse operator for source localization.

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.

Estimate cortical sources from an Evoked using a forward model and noise covariance. method: 'dSPM' (default), 'MNE', 'sLORETA', 'eLORETA'. Stores the source estimate (stc) and reports the peak activation time. Pair with mne_make_forward + mne_compute_noise_cov.

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.