EEG Connectivity Lab with Python: From Data to Publication

Learn how to analyze and visualize EEG connectivity using Python through clear explanations, simple code, and real EEG data. EEG connectivity can help us understand how different brain regions communicate. However, the available methods, mathematical concepts, and programming tools can make this topic difficult to approach.

This course provides a practical and beginner-friendly path from the fundamental concepts of EEG connectivity to the creation of publication-quality figures. You will first learn essential concepts such as volume conduction, phase, amplitude. Through guided Python labs, you will explore several widely used forms of EEG connectivity, including:

• Phase-locking value (PLV)

• Phase–amplitude coupling (PAC)

• Power correlation between EEG channels

• Within-frequency and cross-frequency power relationships

• Connectivity matrices and network visualization

• Visual comparison of connectivity between experimental conditions

The practical exercises use real EEG recordings. The code is intentionally kept simple, clearly explained, and easy to adapt to your own EEG datasets. By the end of the course, you will be able to select an appropriate connectivity approach, perform a basic EEG connectivity analysis in Python, interpret the resulting measures, and create clear figures suitable for presentations, reports, or scientific publications.
This course is designed for students, researchers, and professionals in neuroscience, psychology, biomedical engineering, cognitive science, and related fields who have a basic understanding of EEG and want practical experience with connectivity analysis.
This is an introductory course designed to provide a practical understanding of the core concepts of EEG connectivity. The analyses are intentionally kept at a basic level; advanced mathematical theory, complex statistical methods, and specialized connectivity models are beyond the scope of this course.