"Version: 20230801"--Title page verso.

Includes bibliographical references.

1. Anxiety recognition using a new EEG signal analysis approach based on sample density in a Chebyshev chaotic map / Faezeh Daneshmand-Bahman and Ateke Goshvarpour -- 2. Evaluating cognitive load during lexical decision tasks for monolinguals and bilinguals using EEG / Mahesh Veezhinathan, B. Geethanjali, Sukeerthi Sakthivel and RaamaNarayanan AnanthaNarayanan -- 3. Detection of psychological stress using principal component analysis of phonocardiography signals / Amandeep Cheema and Mandeep Singh -- 4. Affective computational advertising based on perceptual metrics / Soujanya Narayana, Shweta Jain, Harish Katti, Roland Goecke and Ramanathan Subramanian -- 5. Machine-learning-based emotion recognition in arousal-valence space using photoplethysmogram signals / Md. Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Md. Shafayet Hossain, Md. Sakib Abrar Hossain and Muhammad E.H. Chowdhury -- 6. EEG-based human emotion classification from channel-wise feature extraction and feature selection / Lucky Odirile Mohutsiwa and Rodrigo S. Jamisola Jr. -- 7. Detection of physiological body movements in affective disorder patients using EEG signals and deep neural networks / Jason Elroy Martis, M.S. Sannidhan, Sunil Kumar Aithal, R. Balasubramani and K.B. Sudeepa -- 8. Voice-enabled real-time affective framework for negative emotion monitoring / Parker Wilmoth and Prabha Sundaravadivel -- 9. Differential diagnosis tool in healthcare application using respiratory sounds and convolutional neural network / Rajkumar Palaniappan, Kenneth Sundaraj, Fizza Ghulam Nabi and Vikneswaran Vijean -- 10. Virtual reality and augmented reality based affective computing applications in healthcare, challenges, and its future direction / Suma Dawn.

Healthcare is one of the most promising applications of affective computing, where advances using biomedical signals have grown rapidly. This comprehensive book begins with an introduction to affective computing and affective computing models, artificial intelligence, probability theory, and statistical learning. It discusses topics such as noise elimination and baseline wandering effects of biomedical signals, and then proceeds to review biomedical signal acquisition and pre-processing. In this book, biomedical signals in affective states are discussed as well as artificial intelligence techniques used to classify biomedical signals, such as artificial neural networks and support vector machines. A discussion of affective computing applications in neurodegenerative diseases and neurological disorders concludes the book. Recent research is discussed alongside future challenges in these areas. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

Researchers and industry professionals in affective computing and biomedical engineering.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Professor Dr. M. Murugappan has worked at Kuwait College of Science and Technology (KCST), Kuwait, as a Full Professor in Electronics in the Department of Electronics and Communication Engineering since 2016. He has received more than 750K in research grants from Malaysia, Kuwait, and the UK. His publications include more than 140 peer-reviewed conference proceedings papers, journal articles, and book chapters. He is interested in affective computing, the Internet of Things (IoT), brain-computer interface, neuromarketing, signal/medical image processing, and artificial intelligence.

Title from PDF title page (viewed on September 5, 2023).