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Brain electrical activity analysis using wavelet-based informational tools

Author

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  • Rosso, O.A
  • Martin, M.T
  • Plastino, A

Abstract

The traditional way of analyzing brain electrical activity, on the basis of Electroencephalography (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In order to overcome this undesirable feature, a quantitative EEG analysis has been developed over the years that introduces objective measures, reflecting not only the characteristics of the brain activity itself but also giving clues concerning the underlying associated neural dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in (i) time, (ii) frequency, and (iii) space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. In the present work we introduce new information tools based on the wavelet transform for the assessment of EEG data as adapted to a non-extensive scenario.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:313:y:2002:i:3:p:587-608
    DOI: 10.1016/S0378-4371(02)00958-5
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    Cited by:

    1. Zunino, L. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2007. "Wavelet entropy of stochastic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 503-512.
    2. Papapetrou, M. & Kugiumtzis, D., 2020. "Tsallis conditional mutual information in investigating long range correlation in symbol sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    3. Guo, Jia-Yi & Cai, Qing & An, Jian-Peng & Chen, Pei-Yin & Ma, Chao & Wan, Jun-He & Gao, Zhong-Ke, 2022. "A Transformer based neural network for emotion recognition and visualizations of crucial EEG channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. 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.

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