Statistical Signal Processing for Neuroscience and Neurotechnology book cover

Statistical Signal Processing for Neuroscience and Neurotechnology

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.

Audience
Signal processing engineers in electrical and electronic engineering; biomedical engineers; applied mathematicians and statisticians; computational neuroscientists

Hardbound, 433 Pages

Published: August 2010

Imprint: Academic Press

ISBN: 978-0-12-375027-3

Reviews

  • "Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."

    György Buzsáki, M.D., Ph.D.

    Center for Molecular and Behavioral Neuroscience

    Rutgers University


Contents

    1. Introduction -- Karim Oweiss
    2. Detection and Classification of Extracellular Action Potential Recordings -- Karim Oweiss and Mehdi Aghagolzadeh
    3. Information-Theoretic Analysis of Neural Data -- Don H. Johnson
    4. Identification of Nonlinear Dynamics in Neural Population Activity -- Dong Song and Theodore W. Berger
    5. Graphical Models of Functional and Effective Neuronal Connectivity -- Seif Eldawlatly and Karim Oweiss
    6. State-Space Modeling of Neural Spike Train and Behavioral Data -- Zhe Chen, Riccardo Barbieri and Emery N. Brown
    7. Neural Decoding for Motor and Communication Prostheses -- Byron M. Yu, Gopal Santhanam, Maneesh Sahani, and Krishna V. Shenoy
    8. Inner Products for Representation and Learning in the Spike Train Domain -- Antonio R. C. Paiva, Il Park, and Jose C. Principe
    9. Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI -- Paul Sajda, Robin I. Goldman, Mads Dyrholm, and Truman R. Brown
    10. Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces -- Rajesh P. N. Rao and Reinhold Scherer
    11. Prediction of Muscle Activity from Cortical Signals to Restore Hand Grasp in Subjects withSpinal Cord Injury -- Emily R. Oby, Christian Ethier, Matt Bauman, Eric J. Perreault, Jason H. Ko, Lee E. Miller

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