Bauer, Markus

Frith, Chris

Hansen, Lars Kai

Konvalinka, Ivana

Roepstorff, Andreas

Stahlhut, Carsten

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Dec 17, 2017Dual EEG of social interaction: a machine learning approach to the two-brain problemKonvalinka, Ivana; Stahlhut, Carsten ; Bauer, Markus ; Hansen, Lars Kai ; Roepstorff, Andreas ; Frith,ChrisPublication date:2012Link back to DTU OrbitCitation (APA):Konvalinka, I., Stahlhut, C., Bauer, M., Hansen, L. K., Roepstorff, A., & Frith, C. (2012). Dual EEG of socialinteraction: a machine learning approach to the two-brain problem. Abstract from 18th Annual Meeting of theOrganization for Human Brain Mapping, Beijing, China.Dual-EEG of social interaction: a machine learning approach to the two-brain problem  Ivana Konvalinka, Carsten Stahlhut, Markus Bauer, Lars Kai Hansen, Andreas Roepstorff, Chris Frith    Introduction   The underlying neural mechanisms of real-time social interactions remain largely unknown. Recent approaches have begun to depart from studying individuals in isolation, and onto exploring behavioural and neural processes in paradigms involving two or more people engaged in an interaction (Konvalinka et al., 2010; Sebanz et al., 2006). However, only a small number of recent studies have explored what goes on in brains of two people simultaneously as they interact (Astolfi et al., 2010; Dumas et al., 2010; De Vico Fallani et al., 2010; Lindenberger et al., 2009). The question still remains whether such quantification can better reveal the neural signatures of social cognition. In our study, we wanted to address this question by quantifying whether we gain more information about the interaction from the two brains.  Methods   32-channel dual-EEG was measured from 9 pairs of participants as they engaged in an interactive finger-tapping task. They were asked to synchronize with an auditory signal coming from the other member of the pair or the computer, by tapping with their right index finger. They experienced two conditions: an interactive ‘coupled’ condition, each receiving feedback of the other person’s tapping; and an ‘uncoupled’ computer-control condition, each receiving feedback from a non-responsive computer. Time-frequency analysis was carried out for the 0.5-30 Hz range for both conditions. Machine-learning approaches were used to identify the brain signals driving the interaction. The raw-power at 10 Hz range during tapping emanating from electrodes of member one and member two were used as features. We combined data from both participants in each pair, and applied logistic regression using feature selection in order to classify the two conditions. Furthermore, we computed cross-correlations between the inter-tap intervals of the two participants’ behavioural tapping data, taking lag -1 and lag +1 correlations coefficients to determine the leader and the follower of the interaction.   Results  Time-frequency analysis revealed a left-motor and right-frontal suppression at 10 Hz during task execution, when carrying the task out interactively in contrast with the uncoupled computer-driven task. The classification identified the first seven (frontal) electrodes consistently as good classifiers, with 85-99% accuracy. There was a tendency for one member’s frontal electrodes to drive the classifier over the other’s, which predicted the leader of the interaction in 8/9 pairs.   Conclusions  This study reveals new neural mechanisms underlying two-person interactions. It also shows how analyzing two interacting brains can give better classification of behaviour; and hence that the whole of two brains is indeed better than the sum of its parts, at disentangling neural signatures of interaction.    References  Astolfi, L., Toppi, J., De Vico Fallani, F., Vecchiato, G., Salinari, S., Mattia, D., et al. (2010) Neoroelectrical hyperscanning measures simultaneous brain activity in humans. Brain Topography, 23(3), 243-256.  De Vico Fallani, F., Nicosia, V., Sinatra, R., Astolfi, L., Cincotti, F., Mattia, D., et al. (2010) Defecting or not defecting: how to “read” human behaviour during cooperative games by EEG measurements. PLoS One, 5(12), e14187.   Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., Garnero, L. (2010) Inter-brain synchronization during social interaction. PLoS One, 5(8), e12166.  Konvalinka, I., Vuust, P., Roepstorff, A., Frith, C. D. (2010) Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Quarterly Journal of Experiment Psychology (Colchester), 63(11), 2220-2230.  Lindenberger, U., Li, S. C., Gruber, W., Muller, V. (2009) Brains swinging in concert: cortical phase synchronization while playing guitar. BMC Neuroscience, 10, 22.   Sebanz N., Bekkering, H. & Knoblich, G. (2006) Joint action: bodies and minds moving together. Trends in Cognitive Sciences, 10(2), 70-76.     

Dual EEG of social interaction: a machine learning approach to the two-brain problem

http://orbit.dtu.dk/ws/files/84867564/Dual_EEG_of_social_interaction.pdf

Dual EEG of social interaction: a machine learning approach to the two-brain problem

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology