Dr. Mohamed Issa, Brain imaging, Best Researcher Award

Doctorate at Basque centre on cognition brain and language, Spain

Professional Profile

Scopus Profile
Orcid Profile

🎓 Education and Early Career

Dr. Mohamed F. Issa earned his Ph.D. in IT in 2020 from the Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Hungary, graduating with the highest honor, “summa cum laude.” His early academic journey began with a Bachelor of Science in Mathematics (2003-2007) and a Master of Science in Mathematics (2011-2015) from the Faculty of Science, Benha University, Egypt.

🧑‍🏫 Academic and Teaching Experience

Starting in 2008, Dr. Issa served as a research assistant and later as a lecturer in the Department of Scientific Computing, Faculty of Computers and Artificial Intelligence at Benha University, Egypt. He has taught a wide range of courses, including:

  • Applied Algorithms
  • Networks and Security
  • Medical Signal Processing
  • Advanced Data Science
  • Human-Computer Interaction

He has also shared his expertise internationally, teaching Biomedical Signal Processing in Hungary and Advanced Neuroimaging Methods in Spain.

🔬 Research and Professional Experience

Currently, Dr. Issa is a post-doctoral researcher at the Basque Center on Cognition, Brain, and Language (BCBL) in San Sebastian, Spain. His research focuses on brain signal processing, real-time data processing, and network visualization and analysis. Notable achievements during his tenure include developing a portable EEG device, optimizing speech envelope synchronization, and implementing advanced EEG data interpretation software.

Previously, he was a postdoctoral researcher at the Brain Bioelectrical Imaging Research Laboratory, University of Pannonia, Hungary, and has been involved in international research collaborations with University College Dublin, Ireland.

🏅 Awards and Grants

Dr. Issa has received several accolades, including the Young Investigator Award at a Slovakian conference in 2017. He has also been a recipient of the Erasmus+ Mobility Grant from 2022 to 2025.

🌐 International Collaborations and Visits

His international experience extends beyond research, having visited and collaborated with various institutions and attended key events worldwide, including:

  • Trinity College Dublin, Ireland
  • Van Gogh Museum, Netherlands
  • European Parliament, NATO, Belgium
  • The Louvre Museum, France

🌍 Research Interests

My research interests encompass a diverse range of interdisciplinary fields within neuroscience and biomedical informatics. I am particularly focused on the cortical tracking of speech and the development of innovative methods for removing EEG artifacts from ECG and EOG signals. My work involves creating hardware triggers that provide event timestamps for EEG apparatus during bilingual lexical access tasks. Additionally, I am dedicated to mapping topographic brain networks and analyzing neural connectivity between cortical regions during resting states and event-related potentials (ERP).

In my research, I aim to increase the temporal resolution of dynamic functional connectivity, which allows for sharper time-frequency resolution and the ability to track fast-dynamic brain connectivity changes. I have also introduced high-resolution EEG technology as a complement to MRI, aiding in the monitoring and quantifying of stroke patient recovery, alongside clinical stroke scales. Furthermore, I am applying deep learning techniques to brain tumor segmentation to identify similar patterns of connected brain regions and to pinpoint reliable biomarkers that characterize stroke recovery progress and predict outcomes.

📖 Publication Top Noted

Article: Event-Related Spectral Perturbation, Inter Trial Coherence, and Functional Connectivity in motor execution: A comparative EEG study of old and young subjects

    • Authors: Gyulai, A., Körmendi, J., Issa, M.F., Juhasz, Z., Nagy, Z.
    • Journal: Brain and Behavior
    • Volume: 13
    • Issue: 8
    • Pages: e3176
    • Year: 2023

Article: Heartbeat classification based on single lead-II ECG using deep learning

    • Authors: Issa, M.F., Yousry, A., Tuboly, G., AbuEl-Atta, A.H., Selim, M.M.
    • Journal: Heliyon
    • Volume: 9
    • Issue: 7
    • Pages: e17974
    • Year: 2023

Article: Increasing the Temporal Resolution of Dynamic Functional Connectivity with Ensemble Empirical Mode Decomposition

    • Authors: Issa, M.F., Kozmann, G., Juhasz, Z.
    • Journal: IFMBE Proceedings
    • Volume: 80
    • Pages: 664–672
    • Year: 2021

Article: Improved EOG artifact removal using wavelet enhanced independent component analysis

    • Authors: Issa, M.F., Juhasz, Z.
    • Journal: Brain Sciences
    • Volume: 9
    • Issue: 12
    • Pages: 355
    • Year: 2019

Article: Automatic ECG Artefact Removal from EEG Signals

    • Authors: Issa, M.F., Tuboly, G., Kozmann, G., Juhasz, Z.
    • Journal: Measurement Science Review
    • Volume: 19
    • Issue: 3
    • Pages: 101–108
    • Year: 2019

Article: Functional connectivity biomarkers based on resting-state EEG for stroke recovery

    • Authors: Issa, M.F., Gyulai, A., Kozmann, G., Nagy, Z., Juhasz, Z.
    • Journal: Proceedings of the 12th International Conference on Measurement, MEASUREMENT 2019
    • Pages: 133–136
    • Year: 2019
Mohamed Issa | Brain imaging | Best Researcher Award

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