Abstract
The goal of this paper was to assess the impact of learning a new skill on several electroencephalographic (EEG) measurements. In addition, it intended to examine the active brain regions associated with learning. To this effect, changes in brain activity during the pre- and post-learning task were investigated using EEG power spectral density (PSD) and nonlinear features. The evaluated task was learning to type using the Colemak keyboard layout in a twelve-lesson training. 10 participants with a mean age of 29.3 ± 5.7 were included in the experiment. For the first time, Fractal dimension, approximate entropy, and Lyapunov exponents were extracted from a 9-channel EEG signal to characterize brain dynamics. In addition, the PSD of various EEG rhythms was estimated. To identify statistically significant changes in the EEG characteristics, as well as to determine the prominent channels, the t-test was performed. Our results showed maximum EEG power in Beta and Gamma waves; however, the most differences in the power distribution belonged to the Beta band. Among nonlinear features, entropy showed a significant difference in 70% of the EEG channels. This has mainly occurred in the F3, Fz, C3, Cz, POz, and P4 electrodes. Taken together, the results of this study pave the way to evaluate brain dynamics and actively involved areas in pre- and post-learning tasks.
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