Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language disorders. However, infant brain recordings are noisy. As a first step to developing accurate neural biomarkers, we investigate whether infant brain responses to rhythmic stimuli can be classified reliably using EEG from 95 eight-week-old infants listening to natural stimuli (repeated syllables or drumbeats). Both Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches were employed. Applied to one infant at a time, the CNN discriminated syllables from drumbeats with a mean AUC of 0.87, against two levels of noise. The SVM classified with AUC 0.95 and 0.86 respectively, showing reduced performance as noise increased. Our proof-of-concept modelling opens the way to the development of clinical biomarkers for language disorders related to rhythmic entrainment.

目前没有可靠的方法来识别有后期语言障碍风险的婴儿。婴儿对节律性刺激的神经反应可能会提供一种解决方案，因为节律的神经跟踪在患有发育性语言障碍的儿童中是不典型的。然而，婴儿的大脑录音是嘈杂的。作为开发准确神经生物标志物的第一步，我们研究了是否可以使用来自 95 名 8 周大婴儿听自然刺激（重复音节或鼓点）的 EEG 可靠地对婴儿大脑对节律性刺激的反应进行分类。使用了卷积神经网络 (CNN) 和支持向量机 (SVM) 方法。一次应用于一名婴儿，CNN 将音节与鼓声区分开来，平均 AUC 为 0.87，与两个级别的噪音相比。SVM 分类的 AUC 分别为 0.95 和 0.86，表现出随着噪音的增加而降低的性能。我们的概念验证模型为开发与节奏夹带相关的语言障碍的临床生物标志物开辟了道路。