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Related Experiment Video

Updated: Dec 11, 2025

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

Motor Cortex Mapping using Active Gaussian Processes.

Razieh Faghihpirayesh¹, Tales Imbiriba¹, Mathew Yarossi²

¹ECE, Northeastern University, Boston, Massachusetts.

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|August 25, 2020

Summary

This summary is machine-generated.

This study introduces an active learning method using Gaussian Processes to optimize transcranial magnetic stimulation (TMS) mapping. The novel approach efficiently identifies optimal stimulation sites, improving cortical mapping accuracy and speed.

Keywords:

Active Learning Gaussian Process Motor Cortex Motor Evoked Potentials Transcranial Magnetic Stimulation

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Published on: October 24, 2012

Functional Mapping with Simultaneous MEG and EEG

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Last Updated: Dec 11, 2025

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

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Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Transcranial magnetic stimulation (TMS) is used for cortical motor mapping.
Current TMS mapping methods are time-consuming and yield sparse data.
Existing methods rely on expert judgment for site selection.

Purpose of the Study:

To develop a novel active learning method for automated TMS mapping.
To improve the efficiency and accuracy of cortical motor topography inference.
To replace user expertise with an automated strategy for selecting stimulation loci.

Main Methods:

Proposed an active Gaussian Process (GP) strategy for locus selection.
Utilized entropy and mutual information (MI) as selection criteria.
Modified GP criteria to incorporate motor evoked potential (MEP) amplitudes.

Main Results:

The proposed active learning strategy outperforms competing methods.
Experimental results demonstrate enhanced performance with real data.
The method is particularly effective when MEP variations are localized.

Conclusions:

The novel active learning method offers a more efficient approach to TMS mapping.
Automated locus selection based on GP can improve mapping accuracy.
This strategy has potential for faster and more precise neurophysiological assessments.