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Updated: Dec 29, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph

Enrico Piovanelli¹, Davide Piovesan², Shouhei Shirafuji^3,4

¹Department of Precision Engineering, The University of Tokyo,113-8656 Hongo, Tokyo, Japan.

Sensors (Basel, Switzerland)

|February 5, 2020

Summary

This study introduces a novel method merging muscle functional MRI (mfMRI) and electromyography (EMG) for muscle activity assessment. The technique accurately estimates muscle activation patterns, showing potential for diagnostics and rehabilitation.

Keywords:

EMG MRI graph theory, electrical network, muscle activity, forearm

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Last Updated: Dec 29, 2025

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Published on: September 3, 2015

Area of Science:

Biomedical Engineering
Medical Imaging
Neuroscience

Background:

Muscle functional MRI (mfMRI) assesses muscle activity via T2-relaxation time shifts.
Electromyography (EMG) provides muscle electrophysiology data.
A combined MRI-EMG technique for muscle pattern estimation is currently lacking.

Purpose of the Study:

To anatomically and quantitatively evaluate a novel method merging mfMRI and EMG.
To assess the validity of muscle pattern estimation using this combined technique.
To demonstrate the method's potential in diagnostic and rehabilitation applications.

Main Methods:

Utilized a resistive network model based on MRI morphology to estimate muscle activation patterns.
Solved an inverse problem using surface EMG (sEMG) data for muscle activation assessment.
Validated results through comparison with physiological data and electrode space fitting.

Main Results:

The combined method explained over 90% of the input sEMG information on average.
Estimated muscle patterns showed anatomical consistency.
Subjectivity in muscle activation patterns was observed among participants.

Conclusions:

The presented method effectively merges mfMRI and EMG for muscle activity analysis.
The technique demonstrates high accuracy in muscle pattern estimation.
This approach holds significant promise for improving diagnostic and rehabilitation strategies in muscle-related conditions.