Multiscale entropy analysis (MSE) is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. MSE has been successfully applied in the literature when measuring autism traits, Alzheimer’s, and schizophrenia. However, until now, there has been no research on MSE applied to children with dyslexia. In this study, we have applied MSE analysis to the EEG data of an experimental group consisting of children with dyslexia as well as a control group consisting of typically developing children and compared the results. The experimental group comprised 16 participants with dyslexia who visited Ankara University Medical Faculty Child Neurology Department, and the control group comprised 20 age-matched typically developing children with no reading or writing problems. MSE was calculated for one continuous 60-s epoch for each experimental and control group’s EEG session data. The experimental group showed significantly lower complexity at the lowest temporal scale and the medium temporal scales than the typically developing group. Moreover, the experimental group received 60 neurofeedback and multi-sensory learning sessions, each lasting 30 min, with Auto Train Brain. Post-treatment, the experimental group’s lower complexity increased to the typically developing group’s levels at lower and medium temporal scales in all channels.

多尺度熵分析（MSE）是一种新颖的基于熵的方法，用于在一定时间范围内测量生理系统中的动力学复杂性。当测量自闭症特征，阿尔茨海默氏症和精神分裂症时，MSE已成功应用于文献中。但是，到目前为止，还没有关于MSE应用于阅读障碍儿童的研究。在这项研究中，我们已将MSE分析应用于由阅读障碍儿童组成的实验组以及由典型发育儿童组成的对照组的EEG数据，并比较了结果。实验组包括16名患有阅读障碍的参与者，他们访问了安卡拉大学医学院儿童神经病学系，对照组则包括20名年龄相匹配的典型发育中的儿童，没有阅读或书写问题。对于每个实验组和对照组的EEG会话数据，均以一个连续60秒的时间计算MSE。实验组在最低的时间尺度和中等的时间尺度上显示出比典型的开发组低得多的复杂性。此外，实验组还接受了60次神经反馈和多感觉学习课程，每节持续30分钟，并使用自动训练大脑。在治疗后，实验组的较低复杂性在所有通道的较低和中等时间尺度上都增加到典型的发育组水平。实验组通过自动训练大脑接受了60次神经反馈和多感觉学习课程，每次持续30分钟。在治疗后，实验组的较低复杂性在所有通道的较低和中等时间尺度上都增加到典型的发育组水平。实验组通过自动训练大脑接受了60次神经反馈和多感觉学习课程，每次持续30分钟。在治疗后，实验组的较低复杂性在所有通道的较低和中等时间尺度上都增加到典型的发育组水平。