Artificial Intelligence-Based Brain-Computer Interface

Artificial Intelligence-Based Brain-Computer Interface

1st Edition - February 1, 2022
  • Editors: Varun Bajaj, G. R. Sinha
  • Paperback ISBN: 9780323911979

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Description

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.

Key Features

  • 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

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Artificial Intelligence, Neural Engineering, and Information Technology. The audience also includes Neurologists who are interested in Brain-Computer Interface

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

Product details

  • No. of pages: 432
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323911979

About the Editors

Varun Bajaj

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.

Affiliations and Expertise

Assistant Professor, Indian Institute of Technology, Design, and Manufacturing, Jabalpur, India

G. R. Sinha

G. R. Sinha, PhD is an Adjunct Professor at International Institute of Information Technology (IIIT) Bangalore, India, and presently deputed as Professor at Myanmar Institute of Information Technology (MIIT), Mandalay, Myanmar. He has published 259 research papers in various international and national journals and conferences. He has edited four books with Elsevier, Springer and IOP; and currently editing seven more books with reputed publishers. He is a Visiting Professor (Honorary) of Sri Lanka Technological Campus Colombo. He is a Senate member of MIIT and also ACM Distinguished Speaker in the field of Digital Signal Processing. His research areas include cognitive science, brain computing, image processing, and data science applications.

Affiliations and Expertise

Adjunct Professor, International Institute of Information Technology (IIIT), Bangalore, India