Advances in Neural Engineering: Brain-Computer Interfaces, Volume Two covers the broad spectrum of neural engineering subfields and applications. The set provides a comprehensive review of dominant feature extraction methods and classification algorithms in the brain-computer interfaces for motor imagery tasks. The book's authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions. The field of neural engineering deals with many aspects of basic and clinical problems associated with neural dysfunction, including sensory and motor information, stimulation of the neuromuscular system to control muscle activation and movement, analysis and visualization of complex neural systems, and more.
Table of Contents
1. Advances in Human Activity Recognition: Harnessing Machine Learning And Deep Learning With Topological Data Analysis 2. Design And Validation Of A Hybrid Programmable Platform For The Acquisition Of Exg Signals 3. FBSE Based Automated Classification of Motor Imagery EEG Signals in Brain-Computer Interface 4. Automated Detection Of Brain Disease Using Quantum Machine Learning 5. A Study Of The Relationship Of Wavelet Transform Parameters And Their Impact On Eeg Classification Performance 6. Bcis For Stroke Rehabilitation 7. Decoding Imagined Speech For Eeg-Based Bci 8. A Comparison Of Deep Learning Methods And Conventional Methods For Classification Of Ssvep Signals In Brain Computer Interface Framework 9. Benchmarking Convolutional Neural Networks On Continuous Eeg Signals: The Case Of Motor Imagery-Based Bci 10. Advancements in The Diagnosis Of Alzheimer’S Disease (Ad) Through Biomarker Detection 11. Alcoholism Identification By Processing The Eeg Signals Using Oscillatory Modes Decomposition And Machine Learning 12. Investigating the role of cortical rhythms in modulating kinematic synergies and exploring their potential for stroke rehabilitation 13. Stimulus-Independent Non-Invasive Bci Based On Eeg Patterns Of Inner Speech 14. A Review of Modern Brain Computer Interface Investigations And Limits 15. Non-Invasive Brain-Computer Interfaces Using Fnirs, Eeg And Hybrid Fnirs/Eeg 16. Eeg-Based Cognitive Fatigue Recognition Via Machine Learning and Analysis Of Multidomain Features 17. Passive Brain-Computer Interfaces for Cognitive and Pathological Brain Physiological States Monitoring And Control 18. Beyond Brainwaves: Recommendations for Integrating Robotics & Virtual Reality for Eeg-Driven Brain-Computer Interface 19. A Sociotechnical Systems Perspective To Support Brain-Computer Interface Development 20. Assessing Systemic Benefit and Risk in The Development Of Bci Neurotechnology 21. Recent Development of Single Channel EEG-Based Automated Sleep Stage Classification: Review And Future Perspectives
Authors
Ayman S. El-Baz University of Louisville, USA. 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). Jasjit Suri Chairman, AtheroPoint LLC, USA.Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General's Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.
