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mne_compute_raw_inverse

Overview​

mne_compute_raw_inverse computes inverse solutions (MNE, dSPM, or sLORETA) from raw or evoked FIFF data using a pre-computed inverse operator. It supports label-restricted source estimation and outputs results as STC files.

This is a C++ port of the original MNE-C tool by Matti Hämäläinen.

Usage​

mne_compute_raw_inverse [options]

Options​

OptionDescription
--in <file>Raw or evoked data input file (required)
--inv <file>Inverse operator file (required)
--snr <value>SNR value to use for regularization (default: 1.0)
--nave <number>Number of averages (default: 1 for raw, from data for evoked)
--set <number>Evoked data set number to process (default: process all)
--bmin <time>Baseline starting time in milliseconds
--bmax <time>Baseline ending time in milliseconds
--label <file>Label file to restrict processing to (can specify multiple)
--labeldir <dir>Process all labels in directory, compute average waveform per label
--out <file>Output file name (needed when using --labeldir)
--picknormalcompPick current component normal to cortex only
--spmUse dSPM noise-normalization method
--sloretaUse sLORETA noise-normalization method
--mricoordOutput source locations in MRI coordinates
--orignamesUse original label file names in channel names
--align_zAlign waveform signs using surface normal information
--labellist <file>Output label name list to specified file

Description​

This tool applies a pre-computed inverse operator to MEG/EEG data to produce source estimates on the cortical surface. The inverse operator must be pre-computed and stored in a FIFF file.

Inverse Methods​

Three methods are available for computing the source estimates:

  • MNE (default) — Standard minimum-norm estimate. Provides current amplitude estimates in physical units (Am).

  • dSPM (--spm flag) — Dynamic Statistical Parametric Mapping. Produces noise-normalized estimates that are dimensionless statistical test variables. Reduces location bias compared to MNE.

  • sLORETA (--sloreta flag) — Standardized Low-Resolution Electromagnetic Tomography. Another noise normalization approach that uses the resolution matrix diagonal for variance estimation.

For mathematical details on these methods, see The Minimum-Norm Estimates.

Regularization (SNR)​

The --snr parameter controls the regularization of the inverse solution. The regularization parameter λ2\lambda^2 is related to the SNR by λ2≈1/SNR2\lambda^2 \approx 1/\text{SNR}^2.

  • Higher SNR → less regularization → noisier but potentially more detailed estimates
  • Lower SNR → more regularization → smoother estimates

For averaged evoked data, typical SNR values are 1.0–3.0. The number of averages (--nave) is automatically taken into account.

Label-Based Analysis​

The --label option restricts the inverse computation to a specific cortical region defined by a FreeSurfer label file. Multiple labels can be specified. This is useful for ROI-based analyses.

The --labeldir option processes all labels in a directory and computes the average source waveform for each label, which is useful for atlas-based analyses.

Output​

The output is written as STC (source estimate) files, which contain the estimated source activity at each source space location over time. These files can be visualized using mne_inspect or other MNE visualization tools.

Examples​

Compute dSPM source estimates from evoked data:

mne_compute_raw_inverse \
    --in sample_audvis-ave.fif \
    --inv sample_audvis-meg-oct6-inv.fif \
    --snr 3.0 \
    --spm

Compute MNE estimates restricted to a label:

mne_compute_raw_inverse \
    --in sample_audvis-ave.fif \
    --inv sample_audvis-meg-oct6-inv.fif \
    --snr 2.0 \
    --label auditory-lh.label \
    --picknormalcomp

Process all labels in a directory:

mne_compute_raw_inverse \
    --in sample_audvis-ave.fif \
    --inv sample_audvis-meg-oct6-inv.fif \
    --snr 3.0 --spm \
    --labeldir labels/ \
    --out label_timecourses

See Also​