---
title: mne_compute_mne
sidebar_label: mne_compute_mne
sidebar_position: 12
---

# 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

```bash
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.
