Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms, and Applications -

. .

Résumé :
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.

Sommaire:
?

PART 1 INTRODUCTION

CHAPTER 1 Introduction to this book

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

CHAPTER 3 Neuroimaging techniques

PART 2 BIOSIGNAL PROCESSING: FROM BIOSIGNALS TO FEATURES'

DATASETS

CHAPTER 4 Pre-processing and feature extraction

CHAPTER 5 Dimensionality reduction

PART 3 COMPUTATIONAL LEARNING (MACHINE LEARNING)

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

CHAPTER 7 Assessing classifier's performance

PART 4 COMPUTATIONAL INTELLIGENCE

CHAPTER 8 Fuzzy logic and fuzzy systems

CHAPTER 9 Neural networks and deep learning

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

introduction

CHAPTER 11 Bio-inspired algorithms

PART 5 APPLICATIONS AND REVIEWS

CHAPTER 12 A survey on EEG-based imagined speech classification

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

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

CHAPTER 15 Emotion recognition: from speech and facial expressions

CHAPTER 16 Trends and applications of ECG analysis and classification

CHAPTER 17 Analysis and processing of infant cry for diagnosis purposes

CHAPTER 18 Physics augmented classification of fNIRS signals

CHAPTER 19 Evaluation of mechanical variables by registration and analysis of

electromyographic activity

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

CHAPTER 21 Attention deficit and hyperactivity disorder classification with EEG and

machine learning

CHAPTER 22 Representation for event-related fMRI