A comprehensive lecture on EEG preprocessing, filtering, ICA, time–frequency analysis, and feature extraction.

A review for Biomedical Signals include EEG, ECG, and EMG

An overview of the most important EEGLAB plugins and how they extend EEG processing capabilities.

A complete lecture explaining how to interpret, visualize, and report EEG analysis results.

Learn how to design clear, attractive graphical abstracts that instantly communicate your research story.

An overview of the most important EEG datasets worldwide — including open-access repositories, formats, and applications.

PhysioNet provides open-access, high-quality physiological datasets  including ECG, EEG, and other biomedical signals to support research, analysis, and algorithm development.

A clear explanation of the Independent Research Project (IRP): objectives, requirements, and how students can succeed.

A curated collection of essential EEGLAB papers and real-world applications in neuroscience and biomedical research.

Watch Part 1 of Arnaud Delorme’s EEGLAB tutorials, introducing the basics of EEG data processing, loading datasets, and navigating the EEGLAB interface. This is the first video in the full training series — remember to continue with the remaining parts in the playlist for complete learning.

Begin with Part 1 of the EEGLAB training series presented by (Engineer. Muawiyah A. Bahhah, a Master student at KAU 2025), introducing the basics of EEG data processing, loading datasets, and navigating the EEGLAB interface. Remember to continue through the playlist to complete the full training sequence.

Start with Part 1 of the EEGLAB training series presented by Engineer, Yahia Othman, (PhD student at KAU), introducing the fundamentals of EEG data processing, dataset loading, and navigating the EEGLAB interface. Remember to continue through the playlist to complete the full training series.

Submission Method:

All students enrolled in the EEGLAB course must submit their work via a Google Drive link, as demonstrated.

Averaged Results:

Submit all EEGLAB averaged results in high quality (300 DPI).

Each file must be clearly named using the following format:

Example: ERP_Average_Rest_5min

Data Source Documentation:

Provide a Word document describing the data source, including all relevant details and a direct link to the dataset.

Manuscript:

Submit a final written report along with a scientific poster.

Use of Existing Papers:

If published or previous papers use the same or a similar dataset, they must be clearly listed and referenced.

Results Section:

Include all EEGLAB-generated results used in your analysis.

Video Demonstration:

Submit a video explanation demonstrating:

How the EEGLAB results were obtained

How EEGLAB was used for comparison and analysis