Analysis of the brain activity in different mental tasks is an important area of research. We used complexity-based analysis to study the changes in brain activity in four mental tasks: relaxation, Stroop color-word, mirror image recognition, and arithmetic tasks. We used fractal theory, sample entropy, and approximate entropy to analyze the changes in electroencephalogram (EEG) signals between different tasks. Our analysis showed that by moving from relaxation to the Stroop color-word, arithmetic, and mirror image recognition tasks, the complexity of EEG signals increases, respectively, reflecting rising brain activity between these conditions. Furthermore, only the fractal theory could decode the significant changes in brain activity between different conditions. Similar analyses can be done to decode the brain activity in case of other conditions.

分析不同心理任务中的大脑活动是一个重要的研究领域。我们使用基于复杂性的分析来研究四种心理任务中大脑活动的变化：放松、斯特鲁普颜色词、镜像识别和算术任务。我们利用分形理论、样本熵和近似熵来分析不同任务之间脑电图（EEG）信号的变化。我们的分析表明，通过从放松转向斯特鲁普色词、算术和镜像识别任务，脑电图信号的复杂性分别增加，反映了这些条件之间大脑活动的增加。此外，只有分形理论才能解码不同条件下大脑活动的显着变化。可以进行类似的分析来解码其他条件下的大脑活动。