Brain-Computer Interfaces

Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents). Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.

• 1. Advances in Human Activity Recognition: Harnessing Machine Learning And Deep Learning With Topological Data Analysis2. Design And Validation Of A Hybrid Programmable Platform For The Acquisition Of Exg Signals3. FBSE Based Automated Classification of Motor Imagery EEG Signals in Brain-Computer Interface4. Automated Detection Of Brain Disease Using Quantum Machine Learning5. A Study Of The Relationship Of Wavelet Transform Parameters And Their Impact On Eeg Classification Performance6. Bcis For Stroke Rehabilitation7. Decoding Imagined Speech For Eeg-Based Bci8. A Comparison Of Deep Learning Methods And Conventional Methods For Classification Of Ssvep Signals In Brain Computer Interface Framework9. Benchmarking Convolutional Neural Networks On Continuous Eeg Signals: The Case Of Motor Imagery-Based Bci10. Advancements in The Diagnosis Of Alzheimer’S Disease (Ad) Through Biomarker Detection11. Alcoholism Identification By Processing The Eeg Signals Using Oscillatory Modes Decomposition And Machine Learning12. Investigating the role of cortical rhythms in modulating kinematic synergies and exploring their potential for stroke rehabilitation13. Stimulus-Independent Non-Invasive Bci Based On Eeg Patterns Of Inner Speech14. A Review of Modern Brain Computer Interface Investigations And Limits15. Non-Invasive Brain-Computer Interfaces Using Fnirs, Eeg And Hybrid Fnirs/Eeg16. Eeg-Based Cognitive Fatigue Recognition Via Machine Learning and Analysis Of Multidomain Features17. Passive Brain-Computer Interfaces for Cognitive and Pathological Brain Physiological States Monitoring And Control18. Beyond Brainwaves: Recommendations for Integrating Robotics & Virtual Reality for Eeg-Driven Brain-Computer Interface19. A Sociotechnical Systems Perspective To Support Brain-Computer Interface Development20. Assessing Systemic Benefit and Risk in The Development Of Bci Neurotechnology21. Recent Development of Single Channel EEG-Based Automated Sleep Stage Classification: Review And Future Perspectives