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Improving cognitive functions of dyslexies using multi-sensory learning and EEG neurofeedback

机译：使用多感觉学习和EEG神经反馈改善阅读障碍的认知功能

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AutoTrainBrain is a neurofeedback and multi-sensory based mobile phone software application, designed in Sabancı University laboratory with the aim of improving the cognitive functions of dyslexic children. It reads electroencephalography (EEG) signals from 14 channels of eMotiv EPOC+ and processes these signals to provide neurofeedback to child for improving the brain signals with visual and auditory cues in real time. AutoTrainBrain software has been applied to a 14-year old dyslexic child, 10 minutes per week for 9 consecutive weeks. The EEG data has been analyzed by using the following three approaches: estimation of single-channel EEG complexity levels (entropy), spectral brain connectivity between two-channels (coherence), single channel relative Alpha band power ratio. Our experimental analysis shows that the proposed brain training system offers improvements based on the measures used in the three approaches mentioned above. This suggests such training may help increase the number of active cortical neurons and improve regional brain connectivity.
机译：AutoTrainBrain是一种基于神经反馈和多感觉的手机软件应用程序，由萨班奇大学实验室设计，旨在改善阅读障碍儿童的认知功能。它从eMotiv EPOC +的14个通道读取脑电图（EEG）信号，并处理这些信号以向儿童提供神经反馈，从而通过视觉和听觉提示实时改善大脑信号。 AutoTrainBrain软件已被应用到一个14岁的阅读障碍儿童，每周10分钟，连续9周。通过使用以下三种方法来分析EEG数据：单通道EEG复杂程度的估计（熵），两通道之间的频谱大脑连通性（相干性），单通道相对Alpha频带功率比。我们的实验分析表明，所提出的大脑训练系统基于上述三种方法中使用的测量方法提供了改进。这表明这种训练可能有助于增加活跃的皮质神经元的数量，并改善区域性大脑的连通性。

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