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Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Ros T, Férat V, Huang Y, Colangelo C, Kia SM, Wolfers T, Vulliemoz S, Michela A.   +7 more
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Subspace identification by orthogonal decomposition

IFAC Proceedings Volumes, 1999
Abstract There is experimental evidence that the N4SID method performs poorly in certain conditions where the past signals (past inputs and past outputs) and future input spaces are nearly parallel. This paper describes a subspace identification technique based on a preliminary orthogonal decomposition of the data spaces which is more robust and ...
CHIUSO, ALESSANDRO, PICCI, GIORGIO
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Probing inputs for subspace identification

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
There is experimental evidence that the standard subspace methods (e.g. the N4SID method) perform poorly in certain conditions where the past signals (past inputs and past outputs) and future input spaces are nearly parallel. Based on an elementary numerical conditioning analysis, the paper describes a class of (system-dependent) input signals (called ...
CHIUSO, ALESSANDRO, PICCI, GIORGIO
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Nonlinear Subspace Model Identification

IFAC Proceedings Volumes, 2004
Abstract Canonical variates state space (CVSS) modeling is a popular subspace linear model identification technique. A nonlinear extension of CVSS modeling approach was proposed (DeCicco and Cinar, 2000) . The modeling procedure consists of two steps: development of a multivariable nonlinear model for a set of latent variables and the linking of the ...
Ali Cinar, Jeffrey DeCicco
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On-line subspace identification

2001 European Control Conference (ECC), 2001
In this paper a recursive technique, based on the subspace state space identification methods, is presented for identification of time-varying systems. The main idea was to develop an iterative algorithm with most of the advantages of this kind of methods in order to deal with real-time applications and minimize the computational burden.
Catarina J. M. Delgado, P. Lopes dos Santos, J. L. Martins de Carvalho   +2 more
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Subspace identification methods and fMRI analysis

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs ...
Jana, Tauchmanova, Martin, Hromcik
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Adaptive radar detection: A subspace identification approach

2010 2nd International Workshop on Cognitive Information Processing, 2010
We address adaptive detection of Swerling 2 pulse trains by an array of antennas. The disturbance is modeled in terms of a state space model and the ideas of subspace identification are used to come up with a GLRT-based detector. Such detector is compared by Monte Carlo simulation with a Kelly's detector derived assuming that returns are temporally ...
BANDIERA, Francesco, D. Orlando, RICCI, Giuseppe, L. L. Scharf   +3 more
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Closed loop subspace system identification

Proceedings of the 36th IEEE Conference on Decision and Control, 2002
We present a general framework for closed loop subspace system identification. This framework consists of two new projection theorems which allow the extraction of non-steady state Kalman filter states and of system related matrices directly from input output data.
P. Van Overschee, B. De Moor
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Open-loop Subspace Identification

2008
Conventionally, a system is modeled by a transfer function, which is a fractional representation of two polynomials with real coefficients, identified using an optimization scheme for a nonlinear least-squares fit to the data, as discussed in Chapter 2.
Biao Huang, Ramesh Kadali
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On-line subspace system identification

Control Engineering Practice, 1993
Abstract Although state space identification techniques offer some unique advantages over traditional system identification methods based on input/output transfer functions, the computational burden of state space subspace identification has prevented its real-time application. The major costs result from the need for the singular value (or sometimes
Y.M. Cho, G. Xu, T. Kailath
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