Brain-Computer Interfaces
Brain-Computer Interfaces
Published on 28. November 2024
Book
Paperback/Softback
498 pages
978-0-323-95439-6 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 25 mm
Weight
849 gr
ISBN-13
978-0-323-95439-6 (9780323954396)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ayman S. El-Baz | Mba Suri
Brain-Computer Interfaces
E-Book
11/2024
Elsevier
€170.00
Available for download
Persons
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.
Content
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
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