Non-linear canonical correlation for joint analysis of MEG signals from two subjects (Articolo in rivista)

Type
Label
  • Non-linear canonical correlation for joint analysis of MEG signals from two subjects (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.3389/fnins.2013.00107 (literal)
Alternative label
  • Campi, Cristina; Parkkonen, Lauri; Hari, Riitta; Hyvarinen, Aapo (2013)
    Non-linear canonical correlation for joint analysis of MEG signals from two subjects
    in Frontiers in neuroscience (Online)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Campi, Cristina; Parkkonen, Lauri; Hari, Riitta; Hyvarinen, Aapo (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 7 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Aalto University; Consiglio Nazionale delle Ricerche (CNR); Aalto University; Aalto University; Aalto University; University of Helsinki (literal)
Titolo
  • Non-linear canonical correlation for joint analysis of MEG signals from two subjects (literal)
Abstract
  • Traditional stimulus-based analysis methods of magnetoencephalography (MEG) data are often dissatisfactory when applied to naturalistic experiments where two or more subjects are measured either simultaneously or sequentially. To uncover the commonalities in the brain activity of the two subjects, we propose a method that searches for linear transformations that output maximally correlated signals between the two brains. Our method is based on canonical correlation analysis (CCA), which provides linear transformations, one for each subject, such that the temporal correlation between the transformed MEG signals is maximized. Here, we present a non-linear version of CCA which measures the correlation of energies and allows for a variable delay between the time series to accommodate, e.g., leader follower changes. We test the method with simulations and with MEG data from subjects who received the same naturalistic stimulus sequence. The method may help analyse future experiments where the two subjects are measured simultaneously while engaged in social interaction. (literal)
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