Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.

中文翻译：

---

通过 EEG 时间和频谱描述符进行阅读障碍诊断：一种异常检测方法

学习困难的诊断是一个具有挑战性的目标。评估过程中涉及大量因素，这些因素在具有相同难度的人群中表现出很大的差异。诊断通常是通过根据专门为此设计的不同神经心理学（基于性能）测试中获得的结果对受试者进行评分来进行的。最常见的疾病之一是发育性阅读障碍 (DD)，这是一种与心理年龄或教育不足无关的阅读技能获得的具体困难。其患病率估计在人口的 5% 至 12% 之间。DD 诊断的传统测试旨在测量阅读过程中涉及的不同行为变量。在本文中，我们提出了一种诊断方法，它不是基于行为变量，而是基于对不同听觉刺激的非自愿神经生理反应。进行的实验使用脑电图 (EEG) 信号来分析从每个电极获取的信号的时间行为和光谱内容，以提取相关（时间和光谱）特征。此外，电极之间提取的特征之间的关系允许推断出类似连接的模型，该模型显示以同步方式处理听觉刺激的大脑区域。然后提出了一种基于使用这些特征的自动编码器的重建残差的异常检测系统。因此，分类是由所提出的系统基于所产生的连接模型的差异进行的，这些模型已被证明是 DD 鉴别诊断的有用工具，也是一种逐步了解 DD 所涉及的大脑过程的方法。结果证实，在与大脑中开发的采样过程相关的特定频率调制非语音刺激以捕获韵律、音节和音素，从而在区分对照组和 DD 受试者的特定频带中产生效果。所提出的方法显示出相对较高的灵敏度，高于 0.6，并且在某些实验中高达 0.9。结果证实，在与大脑中开发的采样过程相关的特定频率调制非语音刺激以捕获韵律、音节和音素，从而在区分对照组和 DD 受试者的特定频带中产生效果。所提出的方法显示出相对较高的灵敏度，高于 0.6，并且在某些实验中高达 0.9。结果证实，在与大脑中开发的采样过程相关的特定频率调制非语音刺激以捕获韵律、音节和音素，从而在区分对照组和 DD 受试者的特定频带中产生效果。所提出的方法显示出相对较高的灵敏度，高于 0.6，并且在某些实验中高达 0.9。