Self-regulation in electroencephalographic signals during an arithmetic performance test: an approach with an rms fluctuation function (Atena Editora)

Abstract

The state of functioning of the brain by self-regulation techniques allows the user to train the corresponding brain functions by different methods, making it possible to condition the brain to balance its functioning and im- prove memory, concentration and confidence. In this study, we investigated 5 individuals based on self-regulation of learning generated by responses to basic arithmetic stimuli, subtraction of two numbers. To do this, we study 5 of active EGG channels during arithmetic tests. For the series generated by the test, we applied the DFA method to assess the autocorrelation of the series, here representing the areas: frontal, central and parietal in two mo- ments of the scales, n ≤ 60 and n > 60 (30 seconds).). We also investigate the rms root mean square function at three moments of the scale, n < 10 (5 seconds), 10 < n < 100 and n > 100 (50 seconds) . The results found revealed non-stationary behavior with Brownian noise transition for n ≤ 60 and persistence for n > 60. With the rms root mean square function, on average, we verified that the central region, when compared to the other re- gions, the results revealed a positive difference for the fluctuation amplitude, with the exception of Cz(2) − Af4(9) em n > 100. Our findings pointed out that modeling DFA and rms function was useful for investigating responses to brain stimuli. Our research is a contribution to EEG analysis and to the areas of biophysics, systems analysis and digital signal processing.