Data-driven visualization of multichannel EEG coherence networks based on community structure analysis.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Electroencephalography (EEG) coherence networks map functional brain connectivity using signal coherence across frequencies.
• Traditional visualizations (matrices, node-link diagrams) lack spatial detail and can be visually cluttered, especially for multichannel EEG.
• Existing methods struggle to integrate spatial information effectively with functional connectivity data.

Purpose of the Study:

• To propose a novel data-driven visualization method for multichannel EEG coherence networks.
• To overcome limitations of conventional representations, such as poor spatial embedding and visual clutter.
• To facilitate the analysis of functional connectivity within and between spatially defined brain regions.

Main Methods:

• A new method partitions electrodes into dense, spatially connected groups.
• This approach preserves spatial relationships between electrode regions.
• The method enables analysis of functional connectivity within and between these grouped regions.

Main Results:

• The proposed visualization method effectively groups spatially connected electrodes.
• It allows for the analysis of functional connectivity patterns within and between brain regions.
• Applied to data from younger and older adults performing a cognitive task, demonstrating its utility.

Conclusions:

• The developed method offers an improved way to visualize multichannel EEG coherence networks.
• It preserves spatial information and reduces visual clutter compared to traditional methods.
• This technique can serve as a valuable preprocessing step for detailed EEG coherence network analysis and exploring brain structure-function relationships.