Data fusion for paroxysmal events' classification from EEG.

Résumé : Spatiotemporal analysis of electroencephalography is commonly used for classification of events since it allows capturing dependencies across channels. The significant increase of feature vector dimensionality however introduce noise and thus it does not allow the classification models to be trained using a limited number of samples usually available in clinical studies.
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Journal of Neuroscience Methods, Elsevier, 2017, 275, pp.55-65. 〈10.1016/j.jneumeth.2016.10.004〉
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https://hal.inria.fr/hal-01426373
Contributeur : Evangelia Zacharaki <>
Soumis le : mercredi 4 janvier 2017 - 14:23:36
Dernière modification le : jeudi 29 mars 2018 - 13:36:02

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Evangelia Pippa, Evangelia I Zacharaki, Michael Koutroumanidis, Vasileios Megalooikonomou. Data fusion for paroxysmal events' classification from EEG.. Journal of Neuroscience Methods, Elsevier, 2017, 275, pp.55-65. 〈10.1016/j.jneumeth.2016.10.004〉. 〈hal-01426373〉

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