Biosignal Processing and Classification Using Computational Learning and Intelligence

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