# Scalable and Sensor-Agnostic Software for Distributed Processing and Visualization of Multi-Site MEG/EEG Datasets

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $671,289

## Abstract

Project Summary
During the past three decades non-invasive functional brain imaging has developed immensely in terms of
measurement technologies, analysis methods, and innovative paradigms to capture information about brain
function both in healthy and diseased individuals. While functional MRI (fMRI) provides a wealth of information
by measuring the indirect slow hemodynamic signals. Magnetoencephalography (MEG) and
electroencephalography (EEG) remain the only noninvasive techniques capable of directly measuring the
electrophysiological activity directly with a millisecond resolution. During the past twelve years we have
developed, with NIH support, the MNE-Python software, which covers multiple methods of data preprocessing,
source localization, statistical analysis, and estimation of functional connectivity between distributed brain
regions. All algorithms and utility functions are implemented in a consistent manner with well-documented
interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. To further extend
our software to meet the needs of a growing user base and reflect recent developments in MEG/EEG as well
as in invasive electrophysiological recordings. Optically Pumped Magnetometers (OPMs) are sensitive room-
temperature magnetic field sensors that have begun to provide movable, flexible, lightweight, on-scalp MEG
systems, and may soon provide higher signal-to-noise ratio and more complete spatial frequency sampling
than SQUID-based systems. However, analysis tools optimal processing of OPM-MEG data are largely
missing. Therefore, in Aim 1, we will introduce tools for High-Resolution On-Scalp OPM-MEG Data Analysis.
Electrocorticography (ECoG) and subcortical EEG (sEEG) provide focal spatial measurements of the
electrophysiological activity. In Aim 2, we will develop sEEG and ECoG workflows, which includes electrode
localization and intracranial inverse and forward modeling. Recent methodological advances by our group and
the availability of on-scalp OPM-MEG systems (Aim 1) and ECoG/sEEG (Aim 2) have expanded the
possibilities for improved localization of deep (cortical and subcortical) sources in basic and clinical research
applications. In Aim 3, we will introduce these methods to the repertoire of MNE-Python and will use phantom
recordings, human data with known ground truth, and existing MEG databases to validate the new methods.
Finally, in Aim 4, we will continue to develop MNE-Python using best programming practices ensuring
multiplatform compatibility, extensive web-based documentation, training and forums, and hands-on training
workshops.

## Key facts

- **NIH application ID:** 10442915
- **Project number:** 2R01NS104585-05
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** MATTI HAMALAINEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $671,289
- **Award type:** 2
- **Project period:** 2018-08-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442915

## Citation

> US National Institutes of Health, RePORTER application 10442915, Scalable and Sensor-Agnostic Software for Distributed Processing and Visualization of Multi-Site MEG/EEG Datasets (2R01NS104585-05). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10442915. Licensed CC0.

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