Abstract
This paper presents a hybrid approach for extreme artifact detection in electroencephalogram (EEG) data, recorded as part of the polysomnogram (psg). The approach is based on the selection of an “optimal” set of features guided by an evolutionary algorithm and a novelty detector based on Parzen window estimation, whose kernel parameter h is also selected by the evolutionary algorithm. The results here suggest that this approach could be very helpful in cases of absence of artifacts during the training process.
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Fairley, J., Georgoulas, G., Stylios, C., Rye, D. (2010). A Hybrid Approach for Artifact Detection in EEG Data. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_59
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DOI: https://doi.org/10.1007/978-3-642-15819-3_59
Publisher Name: Springer, Berlin, Heidelberg
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