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Related Concept Videos

Brain Waves01:23

Brain Waves

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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: Oct 27, 2025

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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Probing the Functional and Structural Connectivity Underlying EEG Traveling Waves.

Yun Qin1,2, Nan Zhang1, Yan Chen1

  • 1MOE Key Lab for NeuroInformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.

Brain Topography
|July 22, 2021
PubMed
Summary

This study introduces novel ways to measure traveling brain waves using electroencephalography (EEG). These EEG measures correlate with brain connectivity, offering new insights into brain function.

Keywords:
Center of massQuantitative EEGSimultaneous EEG-MRISpatiotemporal dynamicsTraveling EEG

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Related Experiment Videos

Last Updated: Oct 27, 2025

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Neural oscillations are crucial for brain function, regulating activity across multiple scales.
  • Understanding the spatiotemporal dynamics of neural oscillations via traveling electroencephalography (EEG) is vital.
  • Non-invasive characterization of traveling EEG and its link to intrinsic brain connectivity remains underexplored.

Purpose of the Study:

  • To examine traveling EEG properties using the center of mass (EEG-CM) across different frequency bands.
  • To develop quantitative indices for EEG-CM spatiotemporal features: traveling lateralization and velocity.
  • To investigate the relationship between traveling EEG-CM and resting-state functional networks and white matter microstructure using simultaneous EEG-MRI.

Main Methods:

  • Analysis of traveling EEG-CM across various frequency bands on the scalp.
  • Development of novel quantitative indices: EEG-CM lateralization and velocity.
  • Simultaneous EEG-MRI acquisition to correlate EEG-CM dynamics with functional and structural brain connectivity.

Main Results:

  • EEG-CM exhibited similar spatial distributions across frequency bands, with velocity increasing at higher frequencies.
  • Low-frequency EEG-CM lateralization (<30 Hz) showed a negative correlation with the basal ganglia network (BGN).
  • Traveling EEG-CM velocity correlated with fractional anisotropy (FA) in the corpus callosum and corona radiata.

Conclusions:

  • The study provides quantitative EEG indices for characterizing scalp EEG spatiotemporal dynamics.
  • EEG dynamics reflect the functional and structural organization of both cortical and subcortical brain structures.
  • This research bridges the gap between non-invasive EEG measurements and intrinsic brain connectivity.