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(12)
Adaptive Algorithm
Adaptive Filter
Automatic Control
Electroencephalo...
krein space
Least Mean Square
Linear Estimation
lms algorithm
Model Uncertainty
Recursive Least Square
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Robust adaptive techniques for minimization of EOG artefacts from EEG signals
Robust adaptive techniques for minimization of EOG artefacts from EEG signals,10.1016/j.sigpro.2005.10.018,Signal Processing,Sadasivan Puthusserypady,
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Robust adaptive techniques for minimization of EOG artefacts from EEG signals
(
Citations: 7
)
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Sadasivan Puthusserypady
,
Tharmalingam Ratnarajah
In this paper, we propose the application of H∞ techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms (timevarying and exponentiallyweighted) 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 wellknown leastmean square (LMS) and recursive leastsquare (RLS) algorithms. Improvements in the output signaltonoise 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.
Journal:
Signal Processing
, vol. 86, no. 9, pp. 23512363, 2006
DOI:
10.1016/j.sigpro.2005.10.018
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Citation Context
(5)
...Puthusserypady et. al. [
13
] measured the ratio of the power of the artifact signal removed to the EEG signal remaining as a metric for real data, proposing that the higher the ratio, the better the performance of the algorithm...
...Using both the metrics in [
13
] and [14] separately, the performance of an OAR algorithm during periods both with and without OA could be evaluated...
...Puthusserypady et. al. [
13
] to measure how much artifact is removed is combined with a measure of how much an OA removal algorithm is likely to distort underlying EEG in a single metric...
...Puthusserypady et. al. [
13
] proposed the following metric for real EEG:...
Borna Noureddin
,
et al.
Effects of task and EEGbased reference signal on performance of onli...
...Therefore, the subject of signals separation is developed from many years and, specially, the removal of EOG from EEG is emphasized [3,4,5,6,7,
8
,9,10,11,12]...
Robert Krupinski
,
et al.
Estimation of Eye Blinking Using Biopotentials Measurements for Comput...
...Approaches such as trial rejection, eye fixation, EOG subtraction, principal component analysis (PCA) [3], blind source separation (BSS) using ICA [4]‐[6], spatial [7], and H ∞ [
8
] adaptive filters have also...
Kianoush Nazarpour
,
et al.
Removal of the EyeBlink Artifacts From EEGs via STFTS Modeling and R...
...Approaches such as trial rejection, eye fixation, EOG subtraction, principal component analysis (PCA) [3], blind source separation (BSS) using ICA [4], [5], and H∞ [
6
] adaptive filters have also been documented as having varying success...
Kianoush Nazarpour
,
et al.
A Robust Minimum Variance Beamforming Approach for the Removal of the ...
...Approaches such as trial rejection, eye fixation, EOG subtraction, principal component analysis (PCA) [3], blind source separation (BSS) using ICA [4], [5], and H ∞ [
6
] adaptive filters have also been documented as having varying success...
Kianoush Nazarpour
,
et al.
A Robust Minimum Variance Beamforming Approach for the Removal of the ...
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A nonparametric variable stepsize NLMS algorithm for transversal filters
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