Biosignal Processing and Classification Using Computational Learning and Intelligence

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