Biosignal Processing and Classification Using Computational Learning and Intelligence

Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals’ domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others.

• Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs
• Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC
• Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems
• Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing

Dr. Alejandro A. Torres-García is a researcher and a member of the Mexican National System of Researchers Level-1 (2021-2023). His research interests are; biosignals processing and analysis, brain-computer interfaces, silent speech interfaces, machine learning, computational intelligence, and computational thinking. He holds a Ph. D degree in Computer Sciences from the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico. Also, he was an ERCIM postdoctoral researcher at the Norwegian University of Science and Technology in Trondheim, Norway (2019-2020). He has published one book, two book chapters, and about 30 articles in scientific journals and proceedings of national and international conferences. Furthermore, he has done shorts stays as visiting researcher at Freie Universität Berlin (Germany in 2014 and 2015), Università Degli Studi di Firenze (Italy in 2016), Universidad de Jaén (Spain in 2017 and 2018), and Institut National de Recherche en Informatique et en Automatique (INRIA, FRANCE in 2019). He is also a member of the CONACYT thematic networks on Applied Computational Intelligence, and Language Technologies.

Carlos Alberto Reyes García Garcia is a full-time researcher in the Department of Computer Science, the head of the Bio signal Processing and Medical Computing laboratory, and is the founding Coordinator of the Graduate Program in Biomedical Sciences and Technologies as of August of 2017. at the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico since January 2001. He holds a PhD degree in computer science with a specialty in artificial intelligence from Florida State University in Tallahassee, Florida. He is a Level II National Researcher of the National System of Researchers (SNI). He is the national president of the Thematic Network on Applied Computational Intelligence from 2016 to date, IEEE Senior Member and AMEXCOMP invited member He was President of the board of directors of the Mexican Society of Artificial Intelligence (SMIA) and now is an Emeritus Member. His areas of particular research interest are; Computational Intelligence, Bio signal Processing and Classification, Processing, Analysis and Classification of Speech, Analysis and Recognition of Baby's Cry, and Classification of Patterns in General.

Dr. Luis Villaseñor-Pineda is a full-time researcher in the Department of Computer Science at the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico. He obtained his Ph.D. degree in Computer Science from the Université Joseph Fourier (now Université Grenoble Alpes), France. His research interests focus on finding solutions to provide the computer with capabilities to process human language, including written language, spoken language and new forms of interaction, such as brain-computer interfaces based on imagined speech. He is the author of more than 150 refereed articles on these topics. In addition, he is a member of the National System of Researchers (Level II), the Mexican Academy of Sciences (AMC), the Artificial Intelligence Society (SMIA), the Mexican Academy of Computational Sciences (AMEXCOMP) and the Mexican Association for Natural Language Processing (AMPLN), of which he was president from 2018-2020.

Dr. Omar Mendoza Montoya, is a professor and researcher in the Department of Computer Science at Tecnologico de Monterrey campus Guadalajara, Mexico. He holds a Ph.D. in Computer Science from the Freie Universität Berlin. He was a member of the BrainModes Research Group at the Charité-Medical University of Berlin. His research activities involve the development of brain-computer interfaces for assistive technology, neurorehabilitation, and therapy. At the moment, he leads multiple projects focusing on robotic applications controlled by biosignals for people with mobility limitations and neurological conditions, such as amyotrophic lateral sclerosis (ALS). Other of his interests are signal processing, numerical analysis, optimization, statistical learning, mathematical modeling, and neuroimaging.