An overview of affective computing and affective computing models, artificial intelligence, probability theory, and statistical learning is presented in this comprehensive book. Following a review of biomedical signal acquisition and pre-processing for biomedical signals, topics such as noise elimination and baseline wandering effects are discussed. This book discusses biomedical signals in affective states and artificial intelligence-based methods of biomedical signal classification, including support vector machines and neural networks. In its conclusion, the book discusses recent research in neurodegenerative diseases and neurological disorders, as well as future challenges in these areas.
Researchers and industry professionals in affective computing and biomedical engineering will find this book useful, as it contains both fundamental concepts and recent applications.
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