Detecting synchrony in EEG: A comparative study of functional connectivity measures

Area of Science:

• Neuroscience
• Computational Neuroscience
• Signal Processing

Background:

• Investigating brain connectivity is a key area in neuroscience.
• Electroencephalography (EEG) offers high temporal resolution and low cost for brain function analysis.
• Selecting appropriate connectivity measures is vital for accurate interpretation of complex neural data.

Purpose of the Study:

• To evaluate the performance of 26 functional connectivity measures.
• To identify the most reliable measures for detecting existing brain connections in simulated EEG data.
• To assess measure efficacy under challenging conditions, including non-stationary and noisy nonlinear systems.

Main Methods:

• Comparison of 26 functional connectivity measures using simulated data (Hénon maps and EEG).
• Utilized surrogate data analysis to establish significance thresholds for synchrony detection.
• Evaluated measures based on their ability to detect true connections in stationary and non-stationary datasets.

Main Results:

• No single measure excelled in all tested scenarios.
• Correlation and coherence measures were optimal for stationary data with ample samples.
• S-estimator, correntropy, mean-phase coherence, mutual information, and nonlinear interdependence measures demonstrated reliability for non-stationary data with varying window sizes.

Conclusions:

• The optimal functional connectivity measure depends on data characteristics (stationarity, noise levels, sample size).
• Correlation and S-estimator offer a favorable balance of performance and computational efficiency for large-scale EEG analysis.
• Further research is needed to refine connectivity measures for complex, real-world neural data.