Brain-Computer Interface Systems
eBook - PDF

Brain-Computer Interface Systems

Recent Progress and Future Prospects

  1. 280 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Brain-Computer Interface Systems

Recent Progress and Future Prospects

About this book

Brain-Computer Interface (BCI) systems allow communication based on a direct electronic interface which conveys messages and commands directly from the human brain to a computer. In the recent years, attention to this new area of research and the number of publications discussing different paradigms, methods, signal processing algorithms, and applications have been increased dramatically. The objective of this book is to discuss recent progress and future prospects of BCI systems. The topics discussed in this book are: important issues concerning end-users; approaches to interconnect a BCI system with one or more applications; several advanced signal processing methods (i.e., adaptive network fuzzy inference systems, Bayesian sequential learning, fractal features and neural networks, autoregressive models of wavelet bases, hidden Markov models, equivalent current dipole source localization, and independent component analysis); review of hybrid and wireless techniques used in BCI systems; and applications of BCI systems in epilepsy treatment and emotion detections.

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Yes, you can access Brain-Computer Interface Systems by Reza Fazel-Rezai in PDF and/or ePUB format, as well as other popular books in Informatica & Interazione tra uomo e computer. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Brain-Computer Interface Systems - Recent Progress And Future Prospects
  2. Contents
  3. Preface
  4. Chapter 1 A User Centred Approach for Bringing BCI Controlled Applications to End-Users
  5. Chapter 2 BCI Integration: Application Interfaces
  6. Chapter 3 Adaptive Network Fuzzy Inference Systems for Classification in a Brain Computer Interface
  7. Chapter 4 Bayesian Sequential Learning for EEG-Based BCI Classification Problems
  8. Chapter 5 Optimal Fractal Feature and Neural Network: EEG Based BCI Applications
  9. Chapter 6 Using Autoregressive Models of Wavelet Bases in the Design of Mental Task-Based BCIs
  10. Chapter 7 Client-Centred Music Imagery Classification Based on Hidden Markov Models of Baseline Prefrontal Hemodynamic Responses
  11. Chapter 8 Equivalent-Current-Dipole-Source-Localization-Based BCIs with Motor Imagery
  12. Chapter 9 Sources of Electrical Brain Activity Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery
  13. Chapter 10 A Review of P300, SSVEP, and Hybrid P300/SSVEP Brain- Computer Interface Systems
  14. Chapter 11 Review of Wireless Brain-Computer Interface Systems
  15. Chapter 12 Brain Computer Interface for Epilepsy Treatment
  16. Chapter 13 Emotion Recognition Based on Brain-Computer Interface Systems