Brain-Computer Interfaces

by Ayman S. El-Baz (Editor), MBA Suri PhD (Editor)

Overview

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. - Presents Neural Engineering techniques applied to Signal Processing, including feature extraction methods and classification algorithms in BCI for motor imagery tasks - Includes in-depth technical coverage of disruptive neurocircuitry, including neurocircuitry of stress integration, role of basal ganglia neurocircuitry in pathology of psychiatric disorders, and neurocircuitry of anxiety in obsessive-compulsive disorder - Covers neural signal processing data analysis and neuroprosthetics applications, including EEG-based BCI paradigms, EEG signal processing in anesthesia, neural networks for intelligent signal processing, and a variety of neuroprosthetic applications - Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of signal processing

Product Details

ISBN-13:	9780323954402
Publisher:	Elsevier Science & Technology Books
Publication date:	11/05/2024
Series:	Advances in Neural Engineering
Sold by:	Barnes & Noble
Format:	eBook
Pages:	420
File size:	57 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

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

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From the Publisher

Provides a comprehensive look at advances in Brain-Computer Interfaces for Neural Engineering