MNELAB#

MNELAB is a graphical user interface (GUI) for MNE-Python that provides an intuitive point-and-click interface for EEG/MEG analysis. While independent from the MNE-Python team, MNELAB is actively maintained, peer-reviewed (JOSS publication), and provides seamless integration with Wearable Sensing EDF files.

MNELAB GUI screenshot

The MNELAB GUI displaying a loaded DSI-24 EEG recording.#

Installation#

Download the latest standalone installer for your platform—no Python knowledge required:

Getting Started#

Learn how to work with Wearable Sensing data in MNELAB through step-by-step tutorials covering data loading, processing, and visualization.

Tutorial Roadmap

New to MNELAB? Follow this sequence:

  1. Load Wearable Sensing Data - Export from DSI-Streamer and open in MNELAB

  2. Channel Configuration - Set channel types and reference

  3. Filtering - Remove noise and artifacts

  4. Artifact Handling - Use ICA or manual rejection

  5. Epoching & Event Handling - Analyze event-related data

Already familiar? Use Quick Navigation below to jump to specific topics.

Quick Navigation#

Core Operations

Load data and configure channels

Load Wearable Sensing Data
Data Processing

Filter and preprocess signals

Filtering

Integration with MNE-Python#

Files preprocessed in MNELAB can be loaded directly into MNE-Python for advanced analysis. It is recommended to save your processed data in the FIF format for compatibility.

Load MNELAB-preprocessed file in MNE-Python#
import mne

# Load file preprocessed in MNELAB
raw = mne.io.read_raw_fif('preprocessed_data.fif', preload=True)

# Continue with programmatic analysis
# Apply additional processing, source localization, connectivity analysis, etc.

Resources#