Non-linear canonical correlation for joint analysis of MEG signals from two subjects

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Non-linear canonical correlation for joint analysis of MEG signals from two subjects. / Campi, Cristina; Parkkonen, Lauri; Hari, Riitta; Hyvärinen, Aapo.

In: Frontiers in Neuroscience, Vol. 7, 107, 2013, p. 1-7.

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@article{5d3fe33fc03641999b0796cd8fc7b50e,
title = "Non-linear canonical correlation for joint analysis of MEG signals from two subjects",
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.",
keywords = "canonical correlation anaysis (CCA), non-linear CCA, magnetoencephalography (MEG), social interaction, brain signal processing",
author = "Cristina Campi and Lauri Parkkonen and Riitta Hari and Aapo Hyv{\"a}rinen",
year = "2013",
doi = "10.3389/fnins.2013.00107",
language = "English",
volume = "7",
pages = "1--7",
journal = "Frontiers in Neuroscience",
issn = "1662-4548",

}

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TY - JOUR

T1 - Non-linear canonical correlation for joint analysis of MEG signals from two subjects

AU - Campi, Cristina

AU - Parkkonen, Lauri

AU - Hari, Riitta

AU - Hyvärinen, Aapo

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - canonical correlation anaysis (CCA)

KW - non-linear CCA

KW - magnetoencephalography (MEG)

KW - social interaction

KW - brain signal processing

UR - http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2013.00107/abstract

U2 - 10.3389/fnins.2013.00107

DO - 10.3389/fnins.2013.00107

M3 - Article

VL - 7

SP - 1

EP - 7

JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

SN - 1662-4548

M1 - 107

ER -

ID: 920979