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Time Series Modeling Of Neuroscience Data
768.00 EGP

Time Series Modeling Of Neuroscience Data

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768.00 EGP 

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Category Type
Neurology & Pain Management
ISBN
9781420094602
Author
Tohru Ozaki
Publisher
CRC Press Inc
Description:

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required. Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by ...

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PRODUCT INFORMATION

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    Specifications

    Category Type
    Neurology & Pain Management
    ISBN
    9781420094602
    Item EAN
    2724546131957
    People
    Author
    Tohru Ozaki
    People
    Publisher
    CRC Press Inc
    Category Type
    Neurology & Pain Management
    ISBN
    9781420094602
    Item EAN
    2724546131957
    People
    Author
    Tohru Ozaki
    People
    Publisher
    CRC Press Inc
    Technical Information
    Binding
    Paperback
    Languages and countries
    Book Language
    English
    Read more
  •  

    Description:

    Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.

    Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required. Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include: * A statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more * Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike * A state space modeling method for dynamicization of solutions for the Inverse Problems * A heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis * An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series * An innovation-based method for spatial time series modeling for fMRI data analysis The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role.

    • Time Series Modeling Of Neuroscience Data
    • Author: Tohru Ozaki
    • Publisher: Crc Press Inc.
    • Published: 2012
 

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