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.
1. Brain-Computer Interfaces - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front matter - p. Page iv 6. Copyright - p. Page v 7. Contributors - p. Page v 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : Advances in human activity recognition: Harnessing machine learning and deep learning with topological data analysis - p. Page x 11. Chapter 2 : Design and validation of a hybrid programmable platform for the acquisition of ExG signals (EEG, ECG and EMG) - p. Page 27 12. Chapter 3 : FBSE-based automated classification of motor imagery EEG signals in brain–computer interface - p. Page 54 13. Chapter 4 : Automated detection of brain disease using quantum machine learning - p. Page 86 14. Chapter 5 : A study of the relationship of wavelet transform parameters and their impact on EEG classification performance - p. Page 110 15. Chapter 6 : BCIs for stroke rehabilitation - p. Page 129 16. Chapter 7 : Decoding imagined speech for EEG-based BCI - p. Page 147 17. Chapter 8 : A comparison of deep learning methods and conventional methods for classification of SSVEP signals in brain-computer interface framework - p. Page 170 18. Chapter 9 : Benchmarking convolutional neural networks on continuous EEG signals: The case of motor imagery–based BCI - p. Page 185 19. Chapter 10 : Advancements in the diagnosis of Alzheimer’s disease (AD) through biomarker detection - p. Page 200 20. Chapter 11 : Alcoholism identification by processing the EEG signal using oscillatory modes decomposition and machine learning - p. Page 223 21. Chapter 12 : Investigating the role of cortical rhythms in modulating kinematic synergies and exploring their potential for stroke rehabilitation - p. Page 250 22. Chapter 13 : Stimulus-independent noninvasive BCI based on EEG patterns of inner speech - p. Page 262 23. Chapter 14 : A review on contemporary brain–computer interface researches and limitations - p. Page 281 24. Chapter 15 : Noninvasive brain–computer interfaces using fNIRS, EEG, and hybrid EEG-fNIRS - p. Page 294 25. Chapter 16 : EEG-based cognitive fatigue recognition using relevant multi-domain features and machine learning - p. Page 318 26. Chapter 17 : Passive brain–computer interfaces for cognitive and pathological brain physiological states monitoring and control - p. Page 343 27. Chapter 18 : Beyond brainwaves: Recommendations for integrating robotics and virtual reality for EEG-driven brain–computer interfaces - p. Page 386 28. Chapter 19 : A sociotechnical systems’ perspective to support brain–computer interface development - p. Page 409 29. Chapter 20 : Assessing systemic benefit and risk in the development of BCI neurotechnology - p. Page 428 30. Chapter 21 : Recent development of single-channel EEG-based automated sleep stage classification: Review and future perspectives - p. Page 442 31. Index - p. Page 466
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.
1. Brain-Computer Interfaces - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front matter - p. Page iv 6. Copyright - p. Page v 7. Contributors - p. Page v 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : Advances in human activity recognition: Harnessing machine learning and deep learning with topological data analysis - p. Page x 11. Chapter 2 : Design and validation of a hybrid programmable platform for the acquisition of ExG signals (EEG, ECG and EMG) - p. Page 27 12. Chapter 3 : FBSE-based automated classification of motor imagery EEG signals in brain–computer interface - p. Page 54 13. Chapter 4 : Automated detection of brain disease using quantum machine learning - p. Page 86 14. Chapter 5 : A study of the relationship of wavelet transform parameters and their impact on EEG classification performance - p. Page 110 15. Chapter 6 : BCIs for stroke rehabilitation - p. Page 129 16. Chapter 7 : Decoding imagined speech for EEG-based BCI - p. Page 147 17. Chapter 8 : A comparison of deep learning methods and conventional methods for classification of SSVEP signals in brain-computer interface framework - p. Page 170 18. Chapter 9 : Benchmarking convolutional neural networks on continuous EEG signals: The case of motor imagery–based BCI - p. Page 185 19. Chapter 10 : Advancements in the diagnosis of Alzheimer’s disease (AD) through biomarker detection - p. Page 200 20. Chapter 11 : Alcoholism identification by processing the EEG signal using oscillatory modes decomposition and machine learning - p. Page 223 21. Chapter 12 : Investigating the role of cortical rhythms in modulating kinematic synergies and exploring their potential for stroke rehabilitation - p. Page 250 22. Chapter 13 : Stimulus-independent noninvasive BCI based on EEG patterns of inner speech - p. Page 262 23. Chapter 14 : A review on contemporary brain–computer interface researches and limitations - p. Page 281 24. Chapter 15 : Noninvasive brain–computer interfaces using fNIRS, EEG, and hybrid EEG-fNIRS - p. Page 294 25. Chapter 16 : EEG-based cognitive fatigue recognition using relevant multi-domain features and machine learning - p. Page 318 26. Chapter 17 : Passive brain–computer interfaces for cognitive and pathological brain physiological states monitoring and control - p. Page 343 27. Chapter 18 : Beyond brainwaves: Recommendations for integrating robotics and virtual reality for EEG-driven brain–computer interfaces - p. Page 386 28. Chapter 19 : A sociotechnical systems’ perspective to support brain–computer interface development - p. Page 409 29. Chapter 20 : Assessing systemic benefit and risk in the development of BCI neurotechnology - p. Page 428 30. Chapter 21 : Recent development of single-channel EEG-based automated sleep stage classification: Review and future perspectives - p. Page 442 31. Index - p. Page 466
