Statistical Signal Processing for Neuroscience and Neurotechnology
eBook - ePub

Statistical Signal Processing for Neuroscience and Neurotechnology

  1. 433 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Statistical Signal Processing for Neuroscience and Neurotechnology

About this book

This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems

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Yes, you can access Statistical Signal Processing for Neuroscience and Neurotechnology by Karim G. Oweiss in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Signals & Signal Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Table of Contents
  5. Preface
  6. About the Editor
  7. About the Contributors
  8. Chapter 1: Introduction
  9. Chapter 2: Detection and Classification of Extracellular Action Potential Recordings
  10. Chapter 3: Information-Theoretic Analysis of Neural Data
  11. Chapter 4: Identification of Nonlinear Dynamics in Neural Population Activity
  12. Chapter 5: Graphical Models of Functional and Effective Neuronal Connectivity
  13. Chapter 6: State Space Modeling of Neural Spike Train and Behavioral Data
  14. Chapter 7: Neural Decoding for Motor and Communication Prostheses
  15. Chapter 8: Inner Products for Representation and Learning in the Spike Train Domain
  16. Chapter 9: Signal Processing and Machine Learning for Single-Trial Analysis of Simultaneously Acquired EEG and fMRI
  17. Chapter 10: Statistical Pattern Recognition and Machine Learning in Brain–Computer Interfaces
  18. Chapter 11: Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects with Spinal Cord Injury
  19. Index