Artificial Intelligence and BCIs in Healthcare discusses the essentials of state-of-the-art brain signal analysis, AI, and BCIs available today. This book is an ideal bridge of these emerging technologies. Divided into three sections, the chapters cover the Fundamentals, Applications, and Regulatory Issues facing healthcare systems today. Artificial Intelligence and BCIs in Healthcare fills a gap in literature available to researchers and may also be of interest to clinicians.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Preface - p. xiii 8. Chapter 1. Application of artificial intelligence in brain–computer interface and challenges - p. 1 9. Section I. Fundamentals & technology - p. S1 10. Chapter 2. Redefining the paradigm of medical practice with artificial intelligence and brain–computer interface - p. 9 11. Chapter 3. Signal acquisition, signal processing, and modeling - p. 19 12. Chapter 4. Artificial intelligence: Role and relevance in bioengineering - p. 35 13. chapter 5. Nanotechnology and engineered nanomaterials for BCI - p. 55 14. Section II. Applications- adoption & implementation - p. S2 15. Chapter 6. Machine learning approach to imaging-based classification of neurodevelopmental disorders - p. 83 16. Chapter 7. Bayesian optimization to select electrical stimulation parameters for neuromodulation - p. 111 17. Chapter 8. Artificial intelligence: Relevance and applications in dementia - p. 143 18. Chapter 9. Applications of ultrasound and other noninvasive techniques in brain stimulation and imaging - p. 155 19. Chapter 10. Brain–computer interfaces for pediatric and geriatric neurorehabilitation - p. 187 20. Chapter 11. Enhancing point-of-care diagnosis through brain–computer interaction and IoT - p. 205 21. Chapter 12. Single-pulse electrical stimulation in epilepsy and stimulation-evoked potentials in brain–computer interfaces - p. 219 22. Chapter 13. Advancing mental healthcare: AI-driven tools, clinical integration, challenges, and future directions - p. 239 23. Chapter 14. AI in healthcare: Leveraging big data analytics to improve adherence to rehabilitation technology in real-world settings - p. 265 24. Chapter 15. Artificial intelligence and radiology integration in neuromodulation and deep brain stimulation - p. 289 25. Chapter 16. Artificial intelligence for sleep analysis and diagnostics: Current perspectives and future directions - p. 325 26. Chapter 17. Interfaces to write into the brain: Utilizing deep brain stimulation and feedback control to understand and treat neurological conditions - p. 341 27. Chapter 18. Empowering dialysis patients with human-computer interactions: Revolutionizing safety and experience with AI, mobile tech, and wearables - p. 373 28. Section III. Regulatory issues - p. S3 29. Chapter 19. In AI we trust: How to build trustworthy artificial intelligence models - p. 391 30. Index - p. 401
Artificial Intelligence and BCIs in Healthcare discusses the essentials of state-of-the-art brain signal analysis, AI, and BCIs available today. This book is an ideal bridge of these emerging technologies. Divided into three sections, the chapters cover the Fundamentals, Applications, and Regulatory Issues facing healthcare systems today. Artificial Intelligence and BCIs in Healthcare fills a gap in literature available to researchers and may also be of interest to clinicians.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Preface - p. xiii 8. Chapter 1. Application of artificial intelligence in brain–computer interface and challenges - p. 1 9. Section I. Fundamentals & technology - p. S1 10. Chapter 2. Redefining the paradigm of medical practice with artificial intelligence and brain–computer interface - p. 9 11. Chapter 3. Signal acquisition, signal processing, and modeling - p. 19 12. Chapter 4. Artificial intelligence: Role and relevance in bioengineering - p. 35 13. chapter 5. Nanotechnology and engineered nanomaterials for BCI - p. 55 14. Section II. Applications- adoption & implementation - p. S2 15. Chapter 6. Machine learning approach to imaging-based classification of neurodevelopmental disorders - p. 83 16. Chapter 7. Bayesian optimization to select electrical stimulation parameters for neuromodulation - p. 111 17. Chapter 8. Artificial intelligence: Relevance and applications in dementia - p. 143 18. Chapter 9. Applications of ultrasound and other noninvasive techniques in brain stimulation and imaging - p. 155 19. Chapter 10. Brain–computer interfaces for pediatric and geriatric neurorehabilitation - p. 187 20. Chapter 11. Enhancing point-of-care diagnosis through brain–computer interaction and IoT - p. 205 21. Chapter 12. Single-pulse electrical stimulation in epilepsy and stimulation-evoked potentials in brain–computer interfaces - p. 219 22. Chapter 13. Advancing mental healthcare: AI-driven tools, clinical integration, challenges, and future directions - p. 239 23. Chapter 14. AI in healthcare: Leveraging big data analytics to improve adherence to rehabilitation technology in real-world settings - p. 265 24. Chapter 15. Artificial intelligence and radiology integration in neuromodulation and deep brain stimulation - p. 289 25. Chapter 16. Artificial intelligence for sleep analysis and diagnostics: Current perspectives and future directions - p. 325 26. Chapter 17. Interfaces to write into the brain: Utilizing deep brain stimulation and feedback control to understand and treat neurological conditions - p. 341 27. Chapter 18. Empowering dialysis patients with human-computer interactions: Revolutionizing safety and experience with AI, mobile tech, and wearables - p. 373 28. Section III. Regulatory issues - p. S3 29. Chapter 19. In AI we trust: How to build trustworthy artificial intelligence models - p. 391 30. Index - p. 401
