Nidal Kamel

Nidal Kamel, PhD

Associate Professor, Electrical Engineering program, College of Engineering and Computer Science


Dr. Nidal Kamel is a telecommunication specialist majoring in statistical signal processing. He received his master and PhD degrees (Hons) from the Technical University of Gdansk, Poland, in 1989 and1994, respectively. Since 1994 he has been involved in research projects related to statistical signal/image processing, including, estimation theory, stochastic modeling, higher order statistics (HOS), PCA and subspace related theory, independent components analysis, filtering, brain neuroimaging, video tensors processing for background initialization and visual periodicity estimation and magnification, analog and digital telecommunication systems, pattern recognition, and artificial intelligence.
Prior to joining VinUniversity, he held an associate professor position at the Technical University of PETRONAS (UTP), Malaysia and led the Neuro Signal Processing Group at the national Center of Intelligent Signal and Image Research (CISIR). During his affiliation with CISIR he played instrumental role in establishing the center as a research hub in brain signal and image processing in South-East Asia region. His research work concentrated on developing techniques for quantitative assessment of various brain disorders, including stress, anxiety, social anxiety, epilepsy, and alcoholism. This translational type of brain research was conducted in collaboration with local hospitals and medical research centers using different neuroimaging modalities, like Electroencephalography (EEG), Magnetoencephalography (MEG), fMRI, and Functional Near Infra Red (fNIR).
In addition to his active research in brain neuroimaging, he worked closely since 2016 with the autonomous driving research group at University of Technology of Belfort-Montbéliard, France. His work was focused on the development of video tensor processing techniques for background initialization and moving objects detection. The research was conducted for potential implementation in autonomous car industry.

• Satellite Image Processing for Environmental Monitoring
• Statistical Signal and Image Processing
• Estimation Theory and Stochastic Modeling
• Image/signal denoising and video background initialization
• Visual Motion Magnification
• Electroencephalography (EEG) for brain imaging

1. Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian physical & engineering sciences in medicine, 38, 139-149.
2. Subhani, A. R., Mumtaz, W., Saad, M. N. B. M., Kamel, N., & Malik, A. S. (2017). Machine learning framework for the detection of mental stress at multiple levels. IEEE Access, 5, 13545-13556.
3. Jatoi, M. A., Kamel, N., Malik, A. S., Faye, I., & Begum, T. (2014). A survey of methods used for source localization using EEG signals. Biomedical Signal Processing and Control, 11, 42-52.
4. Awang, A., Husain, K., Kamel, N., & Aissa, S. (2017). Routing in vehicular ad-hoc networks: A survey on single-and cross-layer design techniques, and perspectives. IEEE Access, 5, 9497-9517.
5. Nidal, K., & Malik, A. S. (Eds.). (2014). EEG/ERP analysis: methods and applications. Crc Press.
6. Jatoi, M. A., Kamel, N., Malik, A. S., & Faye, I. (2014). EEG based brain source localization comparison of sLORETA and eLORETA. Australasian physical & engineering sciences in medicine, 37, 713-721.
7. Almahasneh, H., Chooi, W. T., Kamel, N., & Malik, A. S. (2014). Deep in thought while driving: An EEG study on drivers’ cognitive distraction. Transportation research part F: traffic psychology and behaviour, 26, 218-226.
8. Amin, H. U., Malik, A. S., Kamel, N., Chooi, W. T., & Hussain, M. (2015). P300 correlates with learning & memory abilities and fluid intelligence. Journal of neuroengineering and rehabilitation, 12(1), 1-14.
9. Al-Ezzi, A., Kamel, N., Faye, I., & Gunaseli, E. (2020). Review of EEG, ERP, and brain connectivity estimators as predictive biomarkers of social anxiety disorder. Frontiers in psychology, 11, 730.
10. Kamel, N. S., Sayeed, S., & Ellis, G. A. (2008). Glove-based approach to online signature verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 1109-1113.

1994: PhD, Statistical Signal Processing, Technical University of Gdansk, Poland
1989: MS, Statistical Signal Processing, Technical University of Gdansk, Poland