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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
A method to study global spatial patterns related to sensory perception in scalp EEG
Yusely Ruiz1, Susan Pockett, Walter J Freeman
1Center for Studies on Electronic and Information Technologies, Universidad Central Marta Abreu de Las Villas, Santa Clara, VC, CP 54830, Cuba. yuselyr@uclv.edu.cu
Journal of Neuroscience Methods
|July 3, 2010
Summary
Spatial patterns in human brain activity, specifically beta oscillations, carry perceptual information during stimulus discrimination. These non-local patterns detected via scalp EEG offer insights for brain-computer interfaces.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Previous animal studies using electrocorticography (ECoG) identified beta and gamma oscillations encoding perceptual information.
- These oscillations modulated amplitude in local and global spatial patterns during conditioned stimulus (CS) discrimination tasks.
Purpose of the Study:
- To investigate if similar spatial amplitude modulation patterns exist in human scalp electroencephalography (EEG) during visual-auditory CS discrimination.
- To adapt ECoG analysis methods for EEG to detect these patterns.
Main Methods:
- Continuous scalp EEG recording from 64 electrodes.
- Band-pass filtering and Hilbert transform to extract analytic amplitude and phase.
- Analysis of beta-gamma spectrum, post-CS onset timing, and scalp topography.
- Classification of spatial patterns based on stimulus type.
Main Results:
- Classifiable spatial patterns of EEG amplitude modulation were identified in all human subjects above chance levels.
- Patterns were detected in the beta frequency range (15-22 Hz), but not gamma.
- These non-local patterns appeared in three distinct bursts after CS onset.
Conclusions:
- Scalp EEG can reveal information about episodically synchronized brain activity related to higher cognitive functions.
- The findings support the potential of EEG for brain-computer interface development.
- The employed methods are suitable for analyzing dense EEG arrays with high spatiotemporal resolution.
