Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for modelling 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. This can help to improve the healthcare system through detection, identification, predication, analysis and classification of disease, management of chronic conditions, and delivery of health services. Artificial Intelligence-Based Brain Computer Interface 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 management of chronic condition, databases and delivery of health services. Various brain image modalities are analyzed and capabilities of the human brain will be exploited in BCI applications and case studies. The book presents AI methods for solving real-world problems and challenges in BCI and healthcare systems with the help of appropriate case studies and research results.

Provides readers with an understanding of the key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders
Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others
Provides readers with illustrative examples of how Artificial Intelligence can be applied to Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders

�ber die Autorinnen und Autoren

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.

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