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