Robust adaptive techniques for minimization of EOG artefacts from EEG signals

In this paper, we propose the application of H ∞ techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms ( time-varying and exponentially-weighted) based on the H ∞ principles are proposed. The idea of applying H ∞...

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Veröffentlicht in:Signal processing Jg. 86; H. 9; S. 2351 - 2363
Hauptverfasser: Puthusserypady, S., Ratnarajah, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.09.2006
Elsevier Science
Schlagworte:
Applied sciences
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Detection, estimation, filtering, equalization, prediction
Electroencephalogram (EEG)
Electrooculogram (EOG) artefacts
Exact sciences and technology
Information, signal and communications theory
Medical management aid. Diagnosis aid
Medical sciences
Robust adaptive filtering
Signal and communications theory
Signal, noise
Telecommunications and information theory
Electrooculogram (EOG) artefacts
Robust adaptive filtering
Electroencephalogram (EEG)
Performance evaluation
H infinite optimization
Adaptive algorithm
Adaptive filtering
Artefact
Electroencephalography
Output signal
Recursive algorithm
Adaptive method
Time variation
Robust control
Algorithm performance
Least squares method
Recursive method
Linear estimation
Least mean squares methods
Signal to noise ratio
ISSN:0165-1684, 1872-7557
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Beschreibung
Zusammenfassung:In this paper, we propose the application of H ∞ techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms ( time-varying and exponentially-weighted) based on the H ∞ principles are proposed. The idea of applying H ∞ techniques is motivated by the fact that they are robust to model uncertainties and lack of statistical information with respect to noise [B. Hassibi, A.H. Sayed, T. Kailath, Linear estimation in Krein spaces—Part 1: theory & Part II: applications, IEEE Trans. Automat. Control 41 (1996) 18–49]. Studies are performed on simulated as well as real recorded signals. Performance of the proposed techniques are then compared with the well-known least-mean square (LMS) and recursive least-square (RLS) algorithms. Improvements in the output signal-to-noise ratio (SNR) along with the time plots are used as criteria for comparing the performance of the algorithms. It is found that the proposed H ∞ -based algorithms work slightly better than the RLS algorithm (especially when the input SNR is very low) and always outperform the LMS algorithm in minimizing the EOG artefacts from corrupted EEG signals.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2005.10.018