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Cross recurrence quantifiers as new connectivity measures for structure learning of Bayesian networks in brain decoding

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  • Yargholi, E.
  • Hossein-Zadeh, G.-A.

Abstract

Bayesian networks were efficiently applied for brain decoding along with connectivity information used in structure learning of Bayesian networks. The modified structure learning proposed expands the application of Bayesian networks in brain-decoding.

Suggested Citation

  • Yargholi, E. & Hossein-Zadeh, G.-A., 2019. "Cross recurrence quantifiers as new connectivity measures for structure learning of Bayesian networks in brain decoding," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 263-274.
    • Handle: RePEc:eee:chsofr:v:123:y:2019:i:c:p:263-274
    DOI: 10.1016/j.chaos.2019.04.019
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    References listed on IDEAS

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    1. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
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