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Cambridge Babylab


The CNE Babylab have been busy! Their new paper titled "Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG" was recently published in Brain and Language and chosen as Editor's choice! Amazing job guys!

In this paper they recorded EEG while 8-weeek old infants listened to rhythmic speech and non-speech. Check it out here:



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.