Full 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.
Contents
PART 1 INTRODUCTION
1. Introduction to this book
2. Biosignals analysis (heart, phonatory system, and muscles)
3. Neuroimaging techniques
PART 2 BIOSIGNAL PROCESSING: FROM BIOSIGNALS TO FEATURES' DATASETS
4. Pre-processing and feature extraction
5. Dimensionality reduction
PART 3 COMPUTATIONAL LEARNING (MACHINE LEARNING)
6. A brief introduction to supervised, unsupervised, and reinforcement learning
7. Assessing classifier's performance
PART 4 COMPUTATIONAL INTELLIGENCE
8. Fuzzy logic and fuzzy systems
9. Neural networks and deep learning
10. Spiking neural networks and dendrite morphological neural networks: an introduction
11. Bio-inspired algorithms
PART 5 APPLICATIONS AND REVIEWS
12. A survey on EEG-based imagined speech classification
13. P300-based brain-computer interface for communication and control
14. EEG-based subject identification with multi-class classification
15. Emotion recognition: from speech and facial expressions
16. Trends and applications of ECG analysis and classification
17. Analysis and processing of infant cry for diagnosis purposes
18. Physics augmented classification of fNIRS signals
19. Evaluation of mechanical variables by registration and analysis of electromyographic activity
20. A review on machine learning techniques for acute leukemia classification
21. Attention deficit and hyperactivity disorder classification with EEG and machine learning
22. Representation for event-related fMRI
