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Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Neurosciences
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Time Series Modeling of Neuroscience Data (Hardcover, New)
Loot Price: R4,685
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Time Series Modeling of Neuroscience Data (Hardcover, New)
Series: Chapman & Hall/CRC Interdisciplinary Statistics
Expected to ship within 10 - 15 working days
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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.
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