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Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Related Experiment Video

Updated: May 11, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Traveling waves in the human visual cortex: An MEG-EEG model-based approach.

Laetitia Grabot^1,2, Garance Merholz¹, Jonathan Winawer^3,4

¹Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France.

Plos Computational Biology

|April 17, 2025

Summary

This summary is machine-generated.

Researchers developed a novel neuroimaging model to detect traveling brain waves using MEG and EEG. This method accurately identifies wave direction and properties, enabling non-invasive study of brain activity and cognition.

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Last Updated: May 11, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Area of Science:

Neuroscience
Computational Neuroscience
Biophysics

Background:

Brain oscillations may propagate as traveling waves in the cortex.
Non-invasive investigation of these waves in humans using MEG/EEG is challenging due to signal complexities.
Lack of ground truth data hinders traveling wave identification.

Purpose of the Study:

To develop and validate a model-based neuroimaging approach for detecting cortical traveling waves non-invasively.
To enable the study of endogenous traveling waves and their cognitive roles using MEG/EEG.

Main Methods:

A two-part model was created: (1) encoding model using fMRI retinotopy to define V1 neural sources, and (2) biophysical head model to project sources onto MEG/EEG sensors.
Model predictions were compared against MEG/EEG data from participants viewing visual stimuli designed to evoke specific traveling or standing waves.

Main Results:

The model demonstrated good performance, correlating predicted and measured sensor data.
Model accuracy was higher for traveling waves aligned with stimulus direction compared to standing waves or oppositely directed traveling waves.
Model performance peaked at stimulation-specific spatial and temporal frequencies.

Conclusions:

The developed model successfully recovers traveling wave properties in the cortex.
This approach provides a robust method for using MEG/EEG to study endogenous traveling waves.
It lays the groundwork for investigating the role of traveling waves in cognitive processes.