Artificial Intelligence-Based Brain-Computer Interface

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 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.

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