Characterization of Electroencephalogram Obtained During the Resolution of Mathematical Operations Using Recurrence Quantification Analysis

• Autor(a) principal: Mendes, A. P.
• Data de Publicação: 2022
• Outros Autores: Jarola, G. M. [UNESP], Oliveira, L. M.A. [UNESP], Gerhardt, G. J.L., Rybarczyk-Filho, J. L. [UNESP], Santos, L. dos
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-030-70601-2_251; http://hdl.handle.net/11449/240888
• topic: EEG; Nonlinear analysis; Recurrence quantification analysis; Resolution of mathematical operation

This work aims to apply recurrence quantification analysis to characterize electroencephalogram signals, in resting and active state situations involving mathematical operation resolution. The best values of the incorporation parameters (delay time, embedding dimension, and threshold) are investigated to obtain the best ranking results. The measures of the recurrence quantification analysis used are recurrence rate, determinism, overage length of diagonal structures, Shannon entropy, laminarity, and maximum length of vertical structures. To compare between resting and active state, the Mann-Whitney test was performed. The results demonstrate that the resting state is predominant in the alpha and theta experiments. Statistical differences were observed in the comparisons between resting and active state for alpha and theta experiments, active state alpha and active state theta experiments and resting and active state for theta experiments.