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Greedy kernel PCA applied to single-channel EEG eecordings
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Resumo(s)
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 mapped data in feature
space. The pre-image of the reconstructed multivariate signal is
computed and the embedding is reverted. The resultant signal is
the high amplitude artifact which must be subtracted from the
original signal to obtain a corrected version of the underlying
signal.