Statistical signal processing for neuroscience and neurotechnology
Author(s)
Bibliographic Information
Statistical signal processing for neuroscience and neurotechnology
Academic Press, an imprint of Elsevier, c2010
Available at 5 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
- Introduction
- Detection and Classification of Extracellular Action Potential Recordings
- Information-Theoretic Analysis of Neural Data
- Identification of Nonlinear Dynamics in Neural Population Activity
- Graphical Models of Functional and Effective Neuronal Connectivity
- State-Space Modeling of Neural Spike Train and Behavioral Data
- Neural Decoding for Motor and Communication Prostheses
- Inner Products for Representation and Learning in the Spike Train Domain
- Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI
- Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces
- Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects with Spinal Cord Injury:
by "Nielsen BookData"