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Localizing hidden regularities with known temporal structure in the EEG evoked response data

Aleksandra Kuznetsova, Elena Krugliakova, Alexei Ossadtchi
doi: https://doi.org/10.1101/093922
Aleksandra Kuznetsova
1Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
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Elena Krugliakova
1Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
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Alexei Ossadtchi
1Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
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Abstract

In this paper we describe a novel data driven spatial filtering technique that can be applied to the ERP analysis in order to find statistically significant hidden differential activations in the EEG data. The technique is based on the known morphological characteristics of the response. Underlying optimization problem is formulated as a generalized Rayleigh quotient maximization problem. We supply our tech-nique with a relevant randomization-based statistical test to assess the significance of the discovered phenomenon. Furthermore, we describe an application of the proposed method to the EEG data acquired in the study devoted to the analysis of the auditory neuroplasticity. We show how the mismatch negativity component, a tiny and short-lasting negative response that hallmarks the novel stimuli activating primary error-detection mechanisms, can be detected after filtration.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 14, 2016.
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Localizing hidden regularities with known temporal structure in the EEG evoked response data
Aleksandra Kuznetsova, Elena Krugliakova, Alexei Ossadtchi
bioRxiv 093922; doi: https://doi.org/10.1101/093922
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Localizing hidden regularities with known temporal structure in the EEG evoked response data
Aleksandra Kuznetsova, Elena Krugliakova, Alexei Ossadtchi
bioRxiv 093922; doi: https://doi.org/10.1101/093922

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