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Discriminating imagined and non-imagined tasks in the motor cortex area: Entropy-complexity plane with a wavelet decomposition

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  • Baravalle, Roman
  • Rosso, Osvaldo A.
  • Montani, Fernando

Abstract

Electroencephalograms reflect the electrical activity of the brain, which can be considered ruled by a chaotic nonlinear dynamics. We consider human electroencephalogram recordings during different motor type activities, and when imagining that they perform this activity. We characterize the different dynamics of the cortex according to distinct motor and imagined movement tasks using an information theory approach and a wavelet decomposition. More specifically, we use the entropy-complexity plane H×C in combination with the wavelet decomposition to precisely quantify the dynamics of the neuronal activity showing that the current theoretical framework allows us to distinguish realized and imagined tasks within the cortex.

Suggested Citation

  • Baravalle, Roman & Rosso, Osvaldo A. & Montani, Fernando, 2018. "Discriminating imagined and non-imagined tasks in the motor cortex area: Entropy-complexity plane with a wavelet decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 27-39.
    • Handle: RePEc:eee:phsmap:v:511:y:2018:i:c:p:27-39
    DOI: 10.1016/j.physa.2018.07.038
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    References listed on IDEAS

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    1. Rosso, Osvaldo A. & Carpi, Laura C. & Saco, Patricia M. & Gómez Ravetti, Martín & Plastino, Angelo & Larrondo, Hilda A., 2012. "Causality and the entropy–complexity plane: Robustness and missing ordinal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 42-55.
    2. Rosso, Osvaldo A. & De Micco, Luciana & Plastino, A. & Larrondo, Hilda A., 2010. "Info-quantifiers’ map-characterization revisited," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4604-4612.
    3. Rosso, O.A & Martin, M.T & Plastino, A, 2002. "Brain electrical activity analysis using wavelet-based informational tools," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 587-608.
    4. Martin, M.T. & Plastino, A. & Rosso, O.A., 2006. "Generalized statistical complexity measures: Geometrical and analytical properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 439-462.
    5. Montani, Fernando & Deleglise, Emilia B. & Rosso, Osvaldo A., 2014. "Efficiency characterization of a large neuronal network: A causal information approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 58-70.
    6. O. A. Rosso & C. Masoller, 2009. "Detecting and quantifying temporal correlations in stochastic resonance via information theory measures," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 69(1), pages 37-43, May.
    7. Rosso, O.A. & Martin, M.T. & Plastino, A., 2005. "Evidence of self-organization in brain electrical activity using wavelet-based informational tools," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 444-464.
    8. Rosso, Osvaldo A & Mairal, Marı́a Liliana, 2002. "Characterization of time dynamical evolution of electroencephalographic epileptic records," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 469-504.
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    Cited by:

    1. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    2. Eduarda T. C. Chagas & Marcelo Queiroz‐Oliveira & Osvaldo A. Rosso & Heitor S. Ramos & Cristopher G. S. Freitas & Alejandro C. Frery, 2022. "White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane," International Statistical Review, International Statistical Institute, vol. 90(2), pages 374-396, August.
    3. Xing, Jieli & Zhang, Yongjie & Chu, Gang & Pan, Qi & Zhang, Xiaotao, 2021. "Detection and reconstruction of catastrophic breaks of high-frequency financial data with local linear scaling approximation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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