Advances in Neural Engineering Volume 2

Brain-Computer Interfaces
Herausgeber: S El-Baz, Ayman; Suri, Jasjit

• Broschiertes Buch

Produktbeschreibung

Neural engineering is an emerging and fast-moving interdisciplinary research area that combines engineering with (a) electronic and photonic technologies, (b) computer science, (c) physics, (d) chemistry, (e) mathematics, and (f) cellular, molecular, cognitive, and behavioral neuroscience. This helps us understand the organizational principles and underlying mechanisms of the biology of neural systems and to further to study the behavioral dynamics and complexity of neural systems in nature. The field of neural engineering deals with many aspects of basic and clinical problems associated with neural dysfunction, including (i) the representation of sensory and motor information, (ii) electrical stimulation of the neuromuscular system to control muscle activation and movement, (iii) the analysis and visualization of complex neural systems at multiscale from the single cell to system levels to understand the underlying mechanisms, (iv) development of novel electronic and photonic devices and techniques for experimental probing, the neural simulation studies, (v) the design and development of human-machine interface systems and artificial vision sensors, and (vi) neural prosthesis to restore and enhance the impaired sensory and motor systems and functions. To highlight this emerging discipline, Dr. Ayman El-Baz and Dr. Jasjit Suri have developed Advances in Neural Engineering, covering the broad spectrum of neural engineering subfields and applications. This Series includes 7 volumes in the following order: Volume 1: Signal Processing Strategies, Volume 2: Brain-Computer Interfaces, Volume 3: Diagnostic Imaging Systems, Volume 4: Brain Pathologies and Disorders, Volume 5: Computing and Data Technologies, Volume 6: Advanced Brain Imaging Techniques and Volume 7: Neural Science Ethics. Volume 1 provides a comprehensive review of dominant feature extraction methods and classification algorithms in the brain-computer interfaces for motor imagery tasks. The authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

Produktdetails

• Verlag: Elsevier Health Sciences
• Seitenzahl: 420
• Erscheinungstermin: 1. November 2024
• Englisch
• ISBN-13: 9780323954396
• ISBN-10: 0323954391
• Artikelnr.: 70898776

Inhaltsangabe

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