Artificial Intelligence and BCIs in Healthcare

Auteur
Dr. Chandra P. Sharma is Adjunct Professor, Department of Pharmaceutical Biotechnology, Manipal College of Pharmaceutical Sciences, Manipal University, and Hon. Emeritus Professor, College of Biomedical Engineering & Applied Sciences, Purbanchal University, Kathmandu, Nepal. Dr. Sharma is a Solid-State Physicist from IIT Delhi and received his training in Biomaterials area in the University of Utah with Prof. D.J. Lyman as a graduate student and in the University of Liverpool, England with Prof. D.F. Williams as a Post-Doctoral Research Associate. Dr. Sharma has been awarded FBSE (Fellow Biomaterials Science & Engineering) by The International Union of Societies for Biomaterials Science & Engineering (IUS-BSE) in 2008 and FBAO (Fellow Biomaterials and Artificial Organs) by Society for Biomaterials & Artificial Organs (India) (SBAOI) in 2011 and shares Whitaker and National Science Foundation Award International Society for Artificial Organs (ISAO) USA, invited member ACS (2015-2018). Dr. Vinoy Thomas is an assistant professor in the Department of Materials Science and Engineering at the University of Alabama at Birmingham. training spans from Chemistry/Polymers to Materials Science and Nanotechnology with specific training and expertise in Polymers and Biomaterials. He also holds secondary appointments at the Department of Biomedical Engineering and Department of Environmental Science & Health. He is also a Senior Research Scientist at the Center for Nanoscale Materials and Biointegration. After his PhD in Biomaterials & Technology he completed postdoctoral training at Friedrich-Schiller University, Germany, and at the National Institute of Standards & Technology (NIST). His research focuses on biomaterials processing-property relationships, nanomaterials for tissue engineering, and nanodiamonds for joint-implants.Arie Nakhmani joined the Department of Electrical and Computer Engineering as an assistant professor in 2013. Prior to that, he completed his postdoctoral training at Georgia Tech and Boston University. He is an Associate Scientist in the Comprehensive Cancer Center, and director of ANRY lab at the UAB. He is the author of over 90 peer-reviewed research articles and book chapters on robust control, machine learning, and signal and image analysis.Rachel June Smith joined the Department of Electrical and Computer Engineering and the Neuroengineering Program as an assistant professor in August 2022. She completed her Ph.D. with Dr. Beth Lopour at UC Irvine in 2019. Her doctoral work focused on the development of computational metrics in scalp EEG data that reflected disease burden and predicted response to treatment in patients with infantile spasms (IS), a potentially devastating epileptic encephalopathy. Rachel completed her postdoctoral training at Johns Hopkins University with Dr. Sridevi Sarma, where she used dynamical systems and control theory techniques to localize the onset of seizures in the epileptic brain from intracranial EEG recordings during single-pulse electrical stimulation. As an assistant professor at UAB, Rachel will continue to collaborate with expert neurologists and use her computational skills to develop novel measures of therapeutic response in patients with neurodegenerative disorders.

Texte du rabat
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.

Contenu

Section 1: Fundamentals

1. Introduction
2. Brain-Computer Interfaces (BCI)
3. Signal Acquisition, Signal Processing, and Modeling
4. Artificial Intelligence in BCI
5. Big Data and BCI
6. Engineering materials in BCI
7. Noninvasive Sensors in BCI
8. Non-Invasive Therapies in BCI
9. Redefining the Paradigm of Medical Practice with Artificial Intelligence and BCI

Section 2: Applications

10. Machine Learning Approach to Imaging-Based Classification of Neurodevelopmental Disorders
11. Tricking the Brain for Non-Invasive Therapies: Smart Mazes
12. Early Diagnostics in Neurodiversity Using AI
13. Applications of Ultrasound and other Non-Invasive Techniques in Brain Stimulation and Imaging
14. BCI, Neuromodulation, and Neurorehabilitation Techniques
15. Fibromyalgia: Understanding the Brain Circuitry Using AI
16. Internet of Things and BCI Related Point of Care Device Applications
17. The Study of Stimulation-Evoked potentials in BCI
18. AI for Sleep Analysis and Diagnostics: Current Perspectives and Future Directions
19. Enhancing Patient Safety in Dialysis Care through Human-Computer Interactions

Section 3: Regulatory Issues

20. Pain Management using AI
21. Future Challenges and Perspectives