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Related Experiment Video

Updated: Jun 20, 2026

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High gamma mapping using EEG.

F Darvas1, R Scherer, J G Ojemann

  • 1Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA. fdarvas@u.washington.edu

Neuroimage
|September 1, 2009
PubMed
Summary
This summary is machine-generated.

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Non-invasive electroencephalography (EEG) successfully maps high gamma (HG) power changes during motor tasks. This technique offers spatially localized signals and bihemispheric phase-locking, crucial for brain-computer interfaces (BCI) and functional mapping.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • High gamma (HG) power changes (above 70 Hz) are vital for motor activity, functional cortical mapping, and brain-computer interface (BCI) control signals.
  • Electrocorticography (ECoG) provides high-quality, localized HG signals but is invasive.
  • Non-invasive methods like electroencephalography (EEG) and magnetoencephalography (MEG) are increasingly explored for detecting task-related HG power changes.

Purpose of the Study:

  • To demonstrate that a 27-channel EEG montage can non-invasively provide high-quality, spatially localized signals for HG frequencies (83-101 Hz).
  • To map HG activity using EEG and compare it with invasive ECoG data.
  • To assess the potential of mapped EEG for functional mapping and BCI applications.

Main Methods:

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Last Updated: Jun 20, 2026

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
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Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

  • Utilized a 27-channel EEG montage with a generic head model and a weighted minimum norm least squares (MNLS) inverse method.
  • Employed a self-paced finger movement paradigm to elicit motor-related activity.
  • Mapped EEG signals onto a generic cortex model to localize HG activity and analyzed phase-locking between motor areas.

Main Results:

  • EEG successfully mapped HG activity to the contralateral motor area with high spatial localization.
  • Observed HG power increases preceding finger movement onset (462 ms and 82 ms before EMG onset).
  • Detected significant bihemispheric phase-locking in HG frequencies, preceding EMG onset by 400 ms, consistent with ECoG findings.

Conclusions:

  • Mapped EEG non-invasively provides high-quality, spatially localized HG power increases during motor tasks.
  • EEG mapping reveals bihemispheric phase-locking, a parameter previously associated mainly with invasive ECoG.
  • These findings support the use of mapped EEG for advanced functional cortical mapping and BCI development.