Academic Press Artificial Intelligence-Based Brain-Computer Interface: Biochemicals and Materials Production from Sustainable Biomass Resources

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. Review Examines the application of Artificial Intelligence to Brain-Computer Interface for the diagnosis and classification of a wide variety of neurological disorders From the Back Cover <i>Artificial Intelligence-Based Brain Computer Interface</i> 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. About the Author Dr. Varun Bajaj is an Assistant Professor in the Discipline of Electronics and Communication, PDPM, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India. His main areas of research are Signal Processing Applications in Biomedical Engineering, Time-Frequency Analysis, Artificial Intelligence, and Brain-Computer Interface. Dr. Bajaj is the author of Analysis ofMedical Modalities for Improved Diagnosis in Modern Healthcare from CRC Press, Modelling and Analysis of Active Biopotential Signals in Healthcare, Volumes 1 and 2from Iop Publishing Ltd, and Computer-Aided Design and Diagnosis Methods for Biomedical Applications from CRC Press. Dr. G R Sinha is Adjunct Professor at International Institute of Information Technology (IIIT) Bangalore, and presently deputed to Myanmar Institute of Information Technology (MIIT) Mandalay.