Abstract：
The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.
摘要：
参与认知功能的神经过程的电活动被捕获在EEG信号中，允许探索跨多个时空尺度的神经元振荡的整合和协调。我们提出了一种新颖的方法，将EEG信号转换为图像序列，考虑涉及低级听觉处理的交叉频率相位同步（CFS）动力学，随着用于检测发展性阅读障碍(DD)的两阶段深度学习模型的发展。这种深度学习模型利用图像序列中保存的空间和时间信息来发现相位同步随时间变化的判别模式，达到高达83％的平衡精度。该结果支持了典型和诵读困难的7岁读者之间存在差异的大脑同步动力学。此外，我们使用一种新的特征掩模获得了可解释的表示,将分类过程中最相关的区域与正常阅读的认知过程以及与阅读障碍中发现的代偿机制相对应的认知过程联系起来.
