Biosignal Processing and Classification Using Computational Learning and Intelligence
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Biosignal Processing and Classification Using Computational Learning and Intelligence

Principles, Algorithms, and Applications

1st Edition - September 18, 2021

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  • Editors: Alejandro Torres Garcia, Carlos Reyes Garcia, Luis Villasenor-Pineda, Omar Mendoza-Montoya
  • eBook ISBN: 9780128204283
  • Paperback ISBN: 9780128201251

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Description

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.

Key Features

  • 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

Readership

Biomedical Engineers and researchers in neural engineering, medical imaging, machine learning, neural networks, and computational intelligence. Students, researchers and clinicians in oncology, epilepsy, and a variety of other specialties

Table of Contents

  • PART 1 INTRODUCTION

    CHAPTER 1 Introduction to this book

    Alejandro A. Torres-García, Omar Mendoza-Montoya, Carlos

    A. Reyes-García, and Luis Villaseñor-Pineda

     

    CHAPTER 2 Biosignals analysis (heart, phonatory system, and muscles)

    Rita Q. Fuentes-Aguilar, Humberto Pérez-Espinosa, and María

    A. Filigrana-de-la-Cruz

     

    CHAPTER 3 Neuroimaging techniques

    Thalía Harmony, María E. Mónica Carlier, and

    Manuel Hinojosa-Rodríguez

    PART 2 BIOSIGNAL PROCESSING: FROM BIOSIGNALS TO FEATURES’

    DATASETS

    CHAPTER 4 Pre-processing and feature extraction

    Alejandro A. Torres-García, Omar Mendoza-Montoya, Marta Molinas,

    Javier M. Antelis, Luis A. Moctezuma, and Tonatiuh Hernández-Del-Toro

    CHAPTER 5 Dimensionality reduction

    Hugo Jair Escalante and Eduardo F. Morales

    PART 3 COMPUTATIONAL LEARNING (MACHINE LEARNING)

    CHAPTER 6 A brief introduction to supervised, unsupervised, and reinforcement learning

    Eduardo F. Morales and Hugo Jair Escalante

     

    CHAPTER 7 Assessing classifier’s performance

    Tonatiuh Hernández-Del-Toro, Fernando Martínez-Santiago, and

    Arturo Montejo-Ráez

    PART 4 COMPUTATIONAL INTELLIGENCE

    CHAPTER 8 Fuzzy logic and fuzzy systems

    Carlos A. Reyes-García and Alejandro A. Torres-García

    CHAPTER 9 Neural networks and deep learning

    A. Pastor López-Monroy and Jesús S. García-Salinas

    CHAPTER 10 Spiking neural networks and dendrite morphological neural networks: an

    introduction

    Humberto Sossa and Carlos D. Virgilio-G.

    CHAPTER 11 Bio-inspired algorithms

    Fernando Wario, Omar Avalos, and Jorge Gálvez

    PART 5 APPLICATIONS AND REVIEWS

    CHAPTER 12 A survey on EEG-based imagined speech classification

    Alejandro A. Torres-García, Carlos A. Reyes-García, and

    Luis Villaseñor-Pineda

    CHAPTER 13 P300-based brain–computer interface for communication and control

    Omar Mendoza-Montoya, Javier M. Antelis, and Jonathan Delijorge

    CHAPTER 14 EEG-based subject identification with multi-class classification

    Luis A. Moctezuma and Marta Molinas

    CHAPTER 15 Emotion recognition: from speech and facial expressions

    Humberto Pérez-Espinosa, Ramón Zatarain-Cabada, and María

    Lucía Barrón-Estrada

    CHAPTER 16 Trends and applications of ECG analysis and classification

    María A. Filigrana-de-la-Cruz

    CHAPTER 17 Analysis and processing of infant cry for diagnosis purposes

    Mario Mandujano Valdes, Orion F. Reyes-Galaviz, Sergio D. Cano Ortiz,

    and Carlos A. Reyes-García

    CHAPTER 18 Physics augmented classification of fNIRS signals

    Felipe Orihuela-Espina, Michelle Rojas-Cisneros, Samuel

    A. Montero-Hernández, Jesús S. García-Salinas, Bibiana Cuervo-Soto, and

    Javier Herrera-Vega

    CHAPTER 19 Evaluation of mechanical variables by registration and analysis of

    electromyographic activity

    Rita Q. Fuentes-Aguilar and Alejandro Garcia-Gonzalez

    CHAPTER 20 A review on machine learning techniques for acute leukemia classification

    Alejandro Rosales-Pérez

    CHAPTER 21 Attention deficit and hyperactivity disorder classification with EEG and

    machine learning

    Claudia Lizbeth Martínez González, Efraín José Martínez Ortiz,

    Jesús Jaime Moreno Escobar, and Juan Alfredo Durand Rivera

    CHAPTER 22 Representation for event-related fMRI

    Claudia Cruz-Martínez and Carlos A. Reyes-García

    Acknowledgments

    References

    Index

Product details

  • No. of pages: 536
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: September 18, 2021
  • Imprint: Academic Press
  • eBook ISBN: 9780128204283
  • Paperback ISBN: 9780128201251

About the Editors

Alejandro Torres Garcia

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.

Affiliations and Expertise

Research Project Collaborator, Instituto Nacional de Astrofísica Optica y Electronica, Puebla, Mexico

Carlos Reyes Garcia

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.

Affiliations and Expertise

Full-Time Researcher, Department of Computer Science, Instituto Nacional de Astrofisica Optica y Electronica, Puebla, Mexico

Luis Villasenor-Pineda

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.

Affiliations and Expertise

Lead Researcher, Department of Computer Science, Instituto Nacional de Astrofisica Optica y Electronica in Puebla, Mexico

Omar Mendoza-Montoya

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.

Affiliations and Expertise

Researcher/Professor, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, N.L., Mexico

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