Tracking of nonstationary EEG with the polynomial root perturbation

Patomaki, L.; Kaipio, Jari P.; Karjalainen, P; Juntunen, Marko

doi:10.1109/iembs.1996.652650

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…There are several recursive algorithms to estimate this kind of model. They are based on the least‐mean‐squares approach, the recursive least‐squares approach (see Mainardi et al, Patomaki et al, and Akay for basic developments; Möller et al for an extension to multivariate and multitrial data; and Astolfi et al, Hesse et al, Tarvainen et al, and Wilke et al for examples of application in neuroscience), and the recursive AR approach . They are all described in detail in Schlögl…”

Section: Time‐varying Granger Causalitymentioning

confidence: 99%

Time, frequency, and time‐varying Granger‐causality measures in neuroscience

Cekic

Grandjean

Renaud

2018

Statistics in Medicine

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This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned.

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Section: Time‐varying Granger Causalitymentioning

confidence: 99%

Time, frequency, and time‐varying Granger‐causality measures in neuroscience

Cekic

Grandjean

Renaud

2018

Statistics in Medicine

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“…However in the case of tracking alpha rhythm of EEG the ARMA model of order p = 6 and q = 2 seems to be suitable. The same model order was also used in [11], [12].…”

Section: Discussionmentioning

confidence: 99%

Tracking of nonstationary EEG with Kalman smoother approach

Tarvainen¹,

Ranta-aho²,

Karjalainen³

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristics of real EEG during an eyes open/closed test. The observed tracking ability of Kalman smoother, compared to other methods considered, seemed to be better.

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