Multivariate approach toward classification of competition and collaboration: An fMRI study

Eun Kyung Jung, Jong-Hwan Lee, Jun Zhang, Soo Young Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This functional magnetic resonance imaging (fMRI) study aimed to distinguish neural activation associated with competition and collaboration using multivoxel pattern analysis (MVPA). For each participant, a searchlight-based MVPA was applied to select informative voxels within training data. The support vector machine with a radial basis function kernel was used to obtain classification accuracy of the informative regions. As a result, within-individual maximum classification performance for the test data reached maximally 94.6%. Important regions classifying competition and collaboration were mainly found within prefrontal cortex (e.g., superior/middle frontal gyri) and visual area (e.g., calcarine sulcus and lingual gyrus). Furthermore, visual regions and dorsolateral prefrontal regions showed average accuracy around 70% across participants. In short, neural contribution during competition or collaboration was characterized as differences in multivoxel pattern with a high accuracy.

Original languageEnglish
Title of host publication3rd International Winter Conference on Brain-Computer Interface, BCI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479974948
DOIs
Publication statusPublished - 2015 Mar 30
Event2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of
Duration: 2015 Jan 122015 Jan 14

Other

Other2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
CountryKorea, Republic of
CityGangwon-Do
Period15/1/1215/1/14

Fingerprint

Occipital Lobe
Prefrontal Cortex
Magnetic Resonance Imaging
Searchlights
Support vector machines
Chemical activation
Support Vector Machine

Keywords

  • collaboration
  • competition
  • Functional magnetic resonance imaging
  • multivoxel pattern analysis
  • searchlight analysis
  • support vector machine

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Sensory Systems

Cite this

Jung, E. K., Lee, J-H., Zhang, J., & Lee, S. Y. (2015). Multivariate approach toward classification of competition and collaboration: An fMRI study. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 [7073044] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2015.7073044

Multivariate approach toward classification of competition and collaboration : An fMRI study. / Jung, Eun Kyung; Lee, Jong-Hwan; Zhang, Jun; Lee, Soo Young.

3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7073044.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jung, EK, Lee, J-H, Zhang, J & Lee, SY 2015, Multivariate approach toward classification of competition and collaboration: An fMRI study. in 3rd International Winter Conference on Brain-Computer Interface, BCI 2015., 7073044, Institute of Electrical and Electronics Engineers Inc., 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015, Gangwon-Do, Korea, Republic of, 15/1/12. https://doi.org/10.1109/IWW-BCI.2015.7073044
Jung EK, Lee J-H, Zhang J, Lee SY. Multivariate approach toward classification of competition and collaboration: An fMRI study. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7073044 https://doi.org/10.1109/IWW-BCI.2015.7073044
Jung, Eun Kyung ; Lee, Jong-Hwan ; Zhang, Jun ; Lee, Soo Young. / Multivariate approach toward classification of competition and collaboration : An fMRI study. 3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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