Gibbon, Samuel, Attaheri, Adam, Ni Choisdealbha, Aine, Rocha, Sinead, Brusini, Perrine ORCID: 0000-0003-0703-7765, Mead, Natasha, Boutris, Panagiotis, Olawole-Scott, Helen, Ahmed, Henna, Flanagan, Sheila et al (show 3 more authors)
(2021)
Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.
BRAIN AND LANGUAGE, 220.
104968-.
Abstract
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Machine Learning, EEG, Convolutional Neural Network, Developmental Language Disorders, Infancy, Rhythm |
Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
Depositing User: | Symplectic Admin |
Date Deposited: | 14 Dec 2021 10:23 |
Last Modified: | 18 Jan 2023 21:19 |
DOI: | 10.1016/j.bl.2021.104968 |
Open Access URL: | https://doi.org/10.1016/j.bandl.2021.104968 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3145340 |