NDLI: Causality in variance in electrophysiological data using the ARCH model

Content Provider	IEEE Xplore Digital Library
Author	Ashrafulla, S. Haldar, J.P. Mosher, J.C. Leahy, R.M.
Copyright Year	2013
Description	Author affiliation: Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA (Mosher, J.C.) \|\| Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA (Ashrafulla, S.; Haldar, J.P.; Leahy, R.M.)
Abstract	Measurements of electrophysiological activity can be used to infer interactions between different regions of the human brain. In this work, we consider the use of an autoregressive conditional heteroscedasticity (ARCH) model to estimate causality in variance between different brain regions in simulation and continuously measured EEG data. We propose an efficient new algorithm for ARCH model estimation and demonstrate that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	798
Ending Page	802
File Size	341833
Page Count	5
File Format	PDF
e-ISBN	9781479923908
DOI	10.1109/ACSSC.2013.6810396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Mathematical model Computational modeling Electroencephalography Biological system modeling Reactive power Time series analysis causality electroencephalography magnetoencephalogra-phy autoregression conditional heteroscedasticity
Content Type	Text
Resource Type	Article

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