MNE-Python

MNE-Python provides processing and analysis of EEG, MEG, stereoelectroencephalography (sEEG), electrocorticography (ECoG), and functional near-infrared spectroscopy (fNIRS) datasets in Python for signal processing, visualization, and source reconstruction in neuroimaging research.

Key Features:

• Supported Modalities: Handles EEG, MEG, sEEG, ECoG, and fNIRS modalities explicitly.
• Data Handling and Scaling: Manages large neuroimaging datasets such as a 306-channel MEG recording at 1,000 Hz (306,000 samples per second).
• Analysis and Visualization: Provides comprehensive signal processing and visualization workflows for neurophysiological data.
• Source Reconstruction: Implements source reconstruction methods for interpreting neural activity patterns.
• Interoperability: Offers functionalities comparable to FieldTrip, Brainstorm, EEGLAB, and SPM.

Scientific Applications:

• EEG/MEG Research: Used for preprocessing, analysis, and visualization of EEG and MEG experimental data.
• Intracranial Data Analysis: Applicable to sEEG and ECoG analysis workflows for invasive recordings.
• fNIRS Analysis: Supports analysis and visualization of functional near-infrared spectroscopy data.
• Source Localization: Employed for source reconstruction and localization studies in neuroscience research.
• Clinical Research (Non-diagnostic): Used in clinical research contexts while noting that certified software is required for diagnostic decisions such as epilepsy diagnosis.

Methodology:

Provides a full analysis pipeline including signal processing, source reconstruction, and visualization.