mne_compute_mne
Overview
mne_compute_mne computes MNE, dSPM, or sLORETA source estimates from evoked data using a pre-computed forward solution and noise covariance. It supports advanced options for label restriction, baseline correction, dipole snapshots, predicted sensor data, and more.
This is a C++ port of the original MNE-C tool by Matti Hämäläinen.
Usage
mne_compute_mne [options]
Options
| Option | Description |
|---|---|
--in <file> | Evoked data input file (required) |
--fwd <file> | Forward solution file (required) |
--cov <file> | Noise covariance matrix file (required) |
--out <file> | Output source estimate file (required) |
--snr <value> | SNR value for regularization |
--method <type> | Inverse method: mne, dSPM, sLORETA |
--label <file> | Restrict estimation to label (repeatable) |
--labeldir <dir> | Process all labels in directory |
--baseline <tmin> <tmax> | Baseline correction interval (ms) |
--collapse | Collapse source estimates across labels |
--scaling <factor> | Scale output data |
--dip <file> | Output dipole snapshot file |
--pick <time> | Extract source estimate at specific time (ms) |
--pred <file> | Output predicted sensor data |
--overwrite | Overwrite output file if it exists |
Description
This tool computes source estimates from evoked data using a forward solution and noise covariance. It supports label-based analysis, baseline correction, dipole snapshots, and predicted sensor data output. The output is compatible with MNE-CPP and MNE-Python.