Artificial Intelligence-Based Brain-Computer Interface

by Varun Bajaj PhD (Editor), G. R. Sinha (Editor)

Overview

Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for the modeling of non-invasive modalities of medical signals such as EEG, MRI and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. The book emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI.

Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in the management of chronic conditions, databases, and in the delivery of health services.

Product Details

ISBN-13:	9780323911979
Publisher:	Elsevier Science
Publication date:	03/08/2022
Pages:	392
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Dr. Varun Bajaj is an Associate Professor in Electronics and Communication Engineering at Maulana Azad National Institute of Technology Bhopal, India, starting January 2024. Previously, he served at the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur from 2014 to 2024, initially as an Assistant Professor and later as an Associate Professor. He earned his Ph.D. in Electrical Engineering from IIT Indore in 2014, following an M.Tech. in Microelectronics and VLSI Design in 2009, and a B.E. in Electronics and Communication Engineering in 2006. Dr. Bajaj holds various editorial roles, including Associate Editor for the IEEE Sensor Journal and Subject Editor-in-Chief for IET Electronics Letters. A Senior Member of IEEE since 2020, he actively reviews for numerous journals and has delivered over 50 expert talks. He has received multiple awards for his research and has been recognized among the top 2% of researchers globally by Stanford University from 2020 to 2023.

Dr. G R Sinha is a Professor at Myanmar Institute of Information Technology (MIIT) Mandalay, Myanmar.
To his credit are 255 research papers, book chapters, and books, including Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, Biomedical Signal Processing for Healthcare Applications, Brain and Behavior Computing, and Data Science and Its Applications from Chapman and Hall/CRC Press, Advances in Biometrics from Springer, and Cognitive Informatics, Volumes 1 and 2, AI-Based Brain Computer Interfaces, and Data Deduplication Approaches from Elsevier Academic Press. He was Dean of Faculty and an Executive Council Member of CSVTU and has served as Distinguished Speaker in the field of Digital Image Processing for the Computer Society of India. His research interests include Applications of Machine Learning and Artificial Intelligence in Medical Image Analysis, Biomedical Signal Analysis, Computer Aided Diagnosis, Computer Vision, and Cognitive Science.

Table of Contents

1. Introduction to Artificial Intelligence and Brain-Computer Interface
2. Development BCI Using AI Diagnosis of Epileptic Seizure Disorders
3. AI-Based BCI for Identification of Sleep Disorders Using EEG Signals
4. Emotion Recognition Based BCI
5. AI-Based BCI for Apnea Detection
6. Motor-Imagery Task Classification in BCI
7. Identifying Alcoholic Brain State and Effect in BCI
8. Approaches for Classification of Apnea Disorders Using EEG Signals
9. Stress Management Using Artificial Intelligence for BCI
10. Machine Learning Techniques for Development of Smart Healthcare
11. Prediction of Disease Based on Probabilistic Modeling of Medical Data
12. AI-Based Classification of Focal Disorders Using EEG Signals
13. Identification and Analysis of EEG Signals for BCI
14. Intelligent Medical Data Processing for BCI
15. Management of Disease Spread in Large Populations: Case Studies in BCI

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From the Publisher

Examines the application of Artificial Intelligence to Brain-Computer Interface for the diagnosis and classification of a wide variety of neurological disorders