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.
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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
