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Greedy kernel PCA applied to single-channel EEG recordings

Tomé, A. M. ; Teixeira, A. R ; Lang, Elmar ; Martins da Silva, A.



Abstract

In this work, we propose the correction of univariate single channel EEGs using a kernel technique. The EEG signal is embedded in its time-delayed coordinates obtaining a multivariate signal. A kernel subspace technique is used for denoising and artefact extraction. The proposed kernel method follows a greedy approach to use a reduced data set to compute a new basis onto which to project the ...

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