Statistical Signal Processing for Neuroscience and Neurotechnology

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.

Key Features

• 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

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

Quotes

"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

Table of Contents

Introduction -- Karim Oweiss
Detection and Classification of Extracellular Action Potential Recordings -- Karim Oweiss and Mehdi Aghagolzadeh
Information-Theoretic Analysis of Neural Data -- Don H. Johnson
Identification of Nonlinear Dynamics in Neural Population Activity -- Dong Song and Theodore W. Berger
Graphical Models of Functional and Effective Neuronal Connectivity -- Seif Eldawlatly and Karim Oweiss
State-Space Modeling of Neural Spike Train and Behavioral Data -- Zhe Chen, Riccardo Barbieri and Emery N. Brown
Neural Decoding for Motor and Communication Prostheses -- Byron M. Yu, Gopal Santhanam, Maneesh Sahani, and Krishna V. Shenoy
Inner Products for Representation and Learning in the Spike Train Domain -- Antonio R. C. Paiva, Il Park, and Jose C. Principe
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
Statistical Pattern Recognition and Machine Learning in Brain-Computer Interfaces -- Rajesh P. N. Rao and Reinhold Scherer
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

Author Information

Edited by Karim G. Oweiss, Associate Professor, Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA