Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG

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....

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Bibliographic Details
Main Authors:	Ahmed, H. (Author), Attaheri, A. (Author), Boutris, P. (Author), Brusini, P. (Author), Flanagan, S. (Author), Gibbon, S. (Author), Goswami, U. (Author), Keshavarzi, M. (Author), Mandke, K. (Author), Mead, N. (Author), Ní Choisdealbha, Á (Author), Olawole-Scott, H. (Author), Rocha, S. (Author)
Format:	Article
Language:	English
Published:	Academic Press Inc. 2021
Subjects:	accuracy algorithm architecture area under the curve Article artificial neural network cesarean section child Child convolutional neural network Convolutional Neural Network developmental language disorder Developmental Language Disorders EEG electroencephalogram electroencephalography Electroencephalography female functional magnetic resonance imaging human Humans Infancy infant Infant latent period learning machine learning Machine Learning male nerve potential Neural Networks, Computer noise principal component analysis psychophysiology Rhythm speech Speech stimulus support vector machine Support Vector Machine training
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Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG

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