Optimization of Sensor and Actuator Locations and Their Sensitivity Analysis

Omatu, Sigeru

doi:10.1016/b978-0-08-037036-1.50088-4

Cited by 1 publication

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References 12 publications

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“…The interaction model could be implemented by replacing the matrix F in the Equation (23) with a suitable linear operator such as differential operator, which acts on the spatial variable r⃗ . The resulting model could then be estimated using infinite-dimensional versions of the Kalman filter and RTS smoother (see, e.g., Omatu and Seinfeld, 1989; Cressie and Wikle, 2002; Kaipio and Somersalo, 2005). Although, the resulting algorithm might be overly complex for physiological noise elimination alone, it might be beneficial when combined with inversion based fMRI methods (cf.…”

Section: Discussionmentioning

confidence: 99%

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

et al. 2012

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In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

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Section: Discussionmentioning

confidence: 99%