Brain functional and effective connectivity based on electroencephalography recordings: A review

Area of Science:

• Neuroscience
• Biomedical Engineering
• Data Science

Background:

• Brain connectivity, including functional and effective connectivity, is crucial for understanding brain networks and neurological conditions.
• Electroencephalography (EEG) is increasingly used to model and estimate brain connectivity.
• A comprehensive review of EEG-based connectivity, especially visualization and machine learning applications for clinical use, is lacking.

Purpose of the Study:

• To review recent studies on EEG-based functional and effective connectivity.
• To explore estimation methods, visualization techniques, and machine learning applications.
• To identify challenges and propose future research directions for clinical translation.

Main Methods:

• Systematic review of EEG-based brain connectivity studies.
• Exploration of linear/nonlinear, parametric/nonparametric, and time/frequency-based estimation methods.
• Review of visualization techniques (Heat Map, data statistics, Head Map) and machine learning classifiers.

Main Results:

• Overview of diverse EEG connectivity estimation techniques.
• Categorization of brain connectivity visualization methods.
• Discussion of machine learning applications for clinical translation of EEG connectivity.

Conclusions:

• EEG-based brain connectivity research is advancing rapidly.
• Visualization and machine learning integration are key for clinical applications.
• Further research is needed to address current challenges and establish clinical utility.