[Eeglablist] Joint ICA in social interaction paradigm

Wed Feb 1 03:45:04 PST 2012

Hi Marco,

I have some experience in working with SOBI- so joint decomposition of 
distant sources based on correlations looks promising to me. 

I am interested in your algorithms performing JBSS.

Thanks and best regards,

Naeem.

--- On Tue, 31/1/12, Marco Congedo <marco.congedo at gmail.com> wrote:

From: Marco Congedo <marco.congedo at gmail.com>
Subject: Re: [Eeglablist] Joint ICA in social interaction paradigm
To: eeglablist at sccn.ucsd.edu
Date: Tuesday, 31 January, 2012, 4:44 PM

Hello,

  we are also working in this field.
The relevant decomposition approach for dual and multi-subject studies in general 
is the joint BSS, to exploit not only classical within-subect source decorrelation, but also the correlation of sources among subjects (between-subject).

If you make a search for "joint blind source separation" articles you will find a few.

See also
Congedo M, Phlypo R, Pham D-T (2011) 

Approximate Joint Singular Value Decomposition of an Asymmetric Rectangular Matrix Set 
IEEE Transactions on Signal Processing 59(1), 415-424. {pdf/ps}

for the particular case N=2.

We are currently in the process of publishing two new algorithms for performing JBSS.for any number of subjects.
If you are interested please let me know.

Cheers,

Marco CONGEDO, 

Research Scientist,

Centre National de la Recherche Scientifique (cnrs) and Grenoble
University.

Team ViBS
(Vision and Brain Signal Processing)

GIPSA-lab (Grenoble Images Parole Signal Automatique)

11 rue des Mathématiques

Domaine universitaire - BP 46 -

38402, Grenoble, France.

tel: +33 (0)4 76 82 62 52 

fax: +33 (0)4 76 57 47 90

http://sites.google.com/site/marcocongedo

On Mon, Jan 30, 2012 at 2:29 AM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com> wrote:

Hello Naeem, Some quick thoughts...These are very early days indeed for two-or more brain studies,especially using EEG, although various groups are making inroads,see recent work Dumas, from Tognoni, and from Babiloni's groups,

among others, all of which use diverse methods with EEG.If you are using ICA, why not decomposeeach person's data separately, without PCA,and then analyze correlations

between similar ICs across the 
two individuals, across particularconditions. "Similar" ICs may be determined via eeglab's study clustering function (which uses PCA)or with the CORRMAP plugin.

Calhoun et al.'s EEGIFT with group-ICAcertainly looks like an interestingoption (attached). Overall, the field is wide open for suggestions,so if you come across some new solutions,

please let the list know!An important point is whether you use measures external to EEG to assess some behavioral metric coordination.Overall the issue is what you are hunting for.

See also Hasson and others' forays into cross-correlation across brains.I've included some brain based citation below which might be of use to you,as well as some non-brain literature that bears directly on two-person or more studies. good luck

Anders et al., (2011). Flow of affective information between communicating brains. NeuroImage 54, 439–446.

Astolfi L, Toppi J, De Vico Fallani F, Vecchiato G, Salinari S, Mattia D, Cincotti F, Babiloni F. (2010). Neuroelectrical Hyperscanning Measures Simultaneous Brain Activity in Humans. Brain Topography, 23 (3), 243-256, 2010.

Dumas G, Nadel J, Soussignan R, Martinerie J, Garnero L (2010) Inter-Brain Synchronization during Social Interaction. PLoS ONE 5(8): e12166

Schilbach L, Wilms M, Eickhoff SB, Romanzetti S, Tepest R, Bente G, Shah NJ, Fink GR, Vogeley K (2009) Minds Made for Sharing: Initiating Joint Attention Recruits Reward-related Neurocircuitry. Journal of Cognitive Neuroscience 0:1-14. 

Schippers MB, Roebroeck A, Renken R, Nanetti L, Keysers C. (2010). "Mapping the Information flow from one brain to another during gestural communication". Proc Natl Acad Sci U S A. 2010 May 18;107(20):9388-93.

Stephens, J. G., Silbert, J. L. & Hasson, U. (2010). Speaker–listener neural coupling underlies successful communication. PNAS, July 27.

Tognoli, E., J. Lagarde, et al. (2007). "The phi complex as a neuromarker of human social coordination." Proc Natl Acad Sci U S A 104(19): 8190-5.

Carletta, J., Hill, R. L., Nicol, C., Taylor, T., de Ruiter, J. P., & Bard, E. G. (2010). Eye tracking for two-person tasks with manipulation of a virtual world. Behavior Research Methods, 42, 254-265. 

Wilms M, Schilbach L, Pfeiffer U, Bente G, Fink GR, Vogeley K: (2010). It´s in your eyes. Using gaze-contingent stimuli to create truly interactive paradigms for social cognitive and affective neuroscience. Social Cognitive and Affective Neuroscience 5, 98-107

Kelso J.A.S, de Guzman G.C., Reveley C., Tognoli E. (2009) Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics. PLoS ONE 4(6) e5749.

Keysers C, Kaas J, Gazzola V. (2010). "Somatosensation in Social Perception." Nature Reviews Neuroscience. 2010 Jun;11(6):417-28. 

Knoblich, G., Butterfill, S., & Sebanz, N. (2011). Psychological research on joint action: theory and data . In B. Ross (Ed.),The Psychology of Learning and Motivation, 54 (pp. 59-101), Burlington: Academic Press.

Kokal I, Keysers C. Granger causality mapping during joint actions reveals evidence for forward models that could overcome sensory-motor delays. PLoS One. 2010 Oct 21;5(10):e13507.

Marsh, K. L., Johnston, L., Richardson, M. J., & Schmidt, R. C. (2009). Hop off the mirror neuron bandwagon and join ours, it’s less crowded! European Journal of Social Psychology, 39, 1234-1235.

Oullier, O., G. C. de Guzman, et al. (2007). "Social coordination dynamics: Measuring human bonding." Social Neuroscience 99999(1): 1 – 15.

Perry, A., Stein, L., & Bentin, S. (2011). Motor and attentional mechanisms involved in social interaction: Evidence from mu and alpha EEG suppression. Neuroimage, 58, 895-904. DOI http://dx.doi.org/10.1016/j.neuroimage.2011.06.060

Richardson, M. J., K. L. Marsh, et al. (2007). "Rocking together: dynamics of intentional and unintentional interpersonal coordination." Hum Mov Sci (6): 867-91.

Richardson, M. J., Marsh, K. L., & Schmidt, R. C. (2010). Challenging the egocentric view of perceiving, acting, and knowing. In L. Feldman Barrett, B. Mesquita, & E. Smith (Eds), The mind in context (pp. 307-333). New York: Guilford Press.

Richardson, M. J., Marsh, K. L., Isenhower, R., Goodman, J., & Schmidt, R. C. (2007). Rocking together: Dynamics of intentional and unintentional interpersonal coordination. Human Movement Science, 26, 867-891.

Richardson, MJ., van der Wel, R.P.R.D., Knoblich, G., & Sebanz, N. (in press). Let the force be with us: Dyads exploit haptic coupling for coordination . Journal of Experimental Psychology: Human Perception and Performance.

On Tue, Jan 24, 2012 at 5:31 AM, muhammad naeem <naeem6500 at yahoo.com> wrote:

Hi EEGlablist,

In an EEG paradigm concerning two-person social interaction I am trying a
 joint ICA approach (e.g: Calhoun and colleagues-NeuroImage 45 (2009) 
S163–S172 and computational Intelligence and Neuroscience 
doi:10.1155/2011/129365 ). A similar approach has been used in other 
studies (e.g Montague and colleagues- NeuroImage 16, 1159–1164 
(2002)doi:10.1006/nimg.2002.1150). Essential difference between two is 
the arrangement of Data. In the first, virtual channels have been 
created (separate sphering process and PCA ) whereas in the second data of two subjects were concatenated 
giving lesser (half  ) IC's to investigate. I am wondering which 
approach is more appropriate and why? 

A subsequent question is regarding data reduction- PCA is usually used 
but as mentioned in the first references may not be suitable for the 
activities which are not time/phase-locked(as the case with my data). 
What are the other options?

Looking forward to your insight.

Best regards,

Naeem.
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-- 
Marco Congedo

http://sites.google.com/site/marcocongedo

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