Time series analysis of hybrid neurophysiological data and application of mutual information

Authors:
Atanu Biswas;Apratim Guha
Affiliations:
Applied Statistics Unit, Indian Statistical Institute, Kolkata, India 700 108;School of Mathematics, University of Birmingham, Birmingham, UK B15 2TT
Venue:
Journal of Computational Neuroscience
Year:
2010

Citing 5
Cited 0

Elements of information theory

Elements of information theory
Convergence properties of functional estimates for discrete distributions

Random Structures & Algorithms - Special issue on analysis of algorithms dedicated to Don Knuth on the occasion of his (100)8th birthday
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
The time-rescaling theorem and its application to neural spike train data analysis

Neural Computation
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multivariate time series data of which some components are continuous time series and the rest are point processes are called hybrid data. Such data sets routinely arise while working with neuroscience data, EEG and spike trains would perhaps be the most obvious example. In this paper, we discuss the modeling of a hybrid time series, with the continuous component being the physiological tremors in the distal phalanx of the middle finger, and motor unit firings in the middle finger portion of the extensor digitorum communis (EDC) muscle. We employ a model for the two components based on Auto-regressive Moving Average (ARMA) type models. Another major issue to arise in the modeling of such data is to assess the goodness of fit. We suggest a visual procedure based on mutual information towards assessing the dependence pattern of hybrid data. The goodness of fit is also verified by standard model fitting diagnostic techniques for univariate data.