On the use of clustering and local singular spectrum analysis to remove ocular artifacts from electroencephalograms

Teixeira, António; Tomé, Ana Maria; Lang, Elmar; Gruber, Peter J.; Silva, António Martins da

doi:10.1109/ijcnn.2005.1556298

Cited by 18 publications

(13 citation statements)

References 12 publications

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“…For example, in (Daly, Nicolaou, et al, 2013) three approaches to artifact removal are compared, including an approach, originally proposed in (Teixeira et al, 2005), for using singular value decomposition as a dimensionality reduction step in an artefact removal method.…”

Section: P R E -P R I N T V E R S I O Nmentioning

confidence: 99%

Translational Algorithms: The Heart of a Brain Computer Interface

Singh

Daly

2014

Brain-Computer Interfaces

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Brain computer Interface (BCI) development encapsulates three basic processes: data acquisition, data processing, and device control. Since the start of the millennium the BCI development cycle has undergone a metamorphosis. This is mainly due to the increased popularity of BCI applications in both commercial and research circles. One of the focuses of BCI research is to bridge the gap between laboratory research and commercial applications using this technology.A vast variety of new approaches are being employed for BCI development ranging from novel paradigms, such as simultaneous acquisitions, through to asynchronous BCI control. The strategic usage of computational techniques, comprising the heart of the BCI system, underwrites this vast range of approaches. This chapter discusses these computational strategies and translational techniques including dimensionality reduction, feature extraction, feature selection, and classification techniques.

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Section: P R E -P R I N T V E R S I O Nmentioning

confidence: 99%

Translational Algorithms: The Heart of a Brain Computer Interface

Singh

Daly

2014

Brain-Computer Interfaces

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“…. , x K ) into a multi-dimensional one, by embedding (a standard procedure for time series analysis [7]) in a high dimensional space [8]. This is done by considering windows of length M < K and N = K − M + 1 lagged vectors…”

Section: B Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

“…It has also been employed for analysis, forecasting and detection of structural changes in time series models. The main domains of its application are the climatic, meteorological and geophysical time series [7] and also biological signals as EEG [8].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Decomposition and Singular Spectrum Analysis for electrical signal denoising

Figueiredo

Almeida

Ribeiro

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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The aggregated electrical load of a household network contains relevant information. Specifically, which loads are related to electrical appliances switched on. However, when real-world data is at stake, not only this specific data must be individually recognized, but also there is other non-relevant information that can be thought as noise in the electrical signal. Therefore, to extract the important information we need to use signal denoising algorithms. This work presents a comparison for the application of an algorithm based on Wavelet Decomposition versus the Singular Spectrum Analysis to the denoise of aggregated electrical signal. These techniques were applied both in an artificially generated signal as well as to the analysis of a signal obtained from an ordinary household. For the latter, the experiments highlighted a small set of wavelet functions that are more suitable for the problem being tackled. Finally, the comparison of the performance of each of the approaches applied to the same sampled signal data indicate the effectiveness of both denoising techniques for use over real data sets.

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“…In order to reduce the complexity we these multidimensional signal vectors together with second present a variant of kernel-PCA whose parameters are computed using the eigendecomposition of a low-rank approximation of with the mean of the values along each descendent diagonal of the kernel matrix. This approach accomplished by splitting the X [8]. Note that if ±c[n] corresponds to the extracted EOG, then data set into subsets.…”

mentioning

confidence: 99%

“…Local SSA basically introduces a clustering step into the SSA of the works (as an example see [7])present solutions while in technique [8] and operates in input space. It encompasses the the work we are proposing a single-channel approach.…”

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confidence: 99%

Single-channel electroencephalogram analysis using non-linear subspace techniques

Teixeira

Alvesf

Tomé

et al. 2007

2007 IEEE International Symposium on Intelligent Signal Processing

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In this work, we propose the correction of univariate, order techniques can be used to decompose the signal into single channel EEGs using projective subspace techniques. The uncorrelated components. The multidimensional signal is then biomedical signals which often represent one dimensional time projected to the most significant directions computed using series, need to be transformed to multi-dimensional signal vectors projecte to tecmositsignificant d rections compedun for the latter techniques to be applicable. The transformation s ingular vposti ( ) pncip componen can be achieved by embedding an observed signal in its delayed analysis (PCA). Singular spectrum analysis (SSA) [4] used coordinates. We propose the application of two non-linear subspace in climatic, meteorologic and geophysics data analysis is the techniques to the obtained multidimensional signal. One of the most widely used technique that follows this strategy. The techniques consists in a modified version of Singular Spectrum general purpose of SSA is to decompose the embedded signal Analysis (SSA) and the other is kernel Principal Component Analvectors into additive components. This can be used to separate ysis (KPCA) implemented using a reduced rank approximation of the kernel matrix. Both nonlinear subspace projection techniques noise contributons from a recorded signal by estmating those are applied to an electroencephalogram (EEG) signal recorded directions, corresponding to the L largest eigenvalues, which in the frontal channel to extract its prominent electrooculogram can be associated with the eigenvectors spanning the signal (EOG) interference.subspace. The remaining orthogonal directions then can be associated with the noise subspace. Reconstructing the signal Keywords -Subspace Techniques, local SSA, KPCA, EOGs, EEG using only those L dominant components then can result in a substantial noise reduction of the recorded signals. The time

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On the use of clustering and local singular spectrum analysis to remove ocular artifacts from electroencephalograms

Cited by 18 publications

References 12 publications

Translational Algorithms: The Heart of a Brain Computer Interface

Translational Algorithms: The Heart of a Brain Computer Interface

Wavelet Decomposition and Singular Spectrum Analysis for electrical signal denoising

Single-channel electroencephalogram analysis using non-linear subspace techniques

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