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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....
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358977/ https://www.ncbi.nlm.nih.gov/pubmed/34111684 http://dx.doi.org/10.1016/j.bandl.2021.104968 |
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author | Gibbon, Samuel Attaheri, Adam Ní Choisdealbha, Áine Rocha, Sinead Brusini, Perrine Mead, Natasha Boutris, Panagiotis Olawole-Scott, Helen Ahmed, Henna Flanagan, Sheila Mandke, Kanad Keshavarzi, Mahmoud Goswami, Usha |
author_facet | Gibbon, Samuel Attaheri, Adam Ní Choisdealbha, Áine Rocha, Sinead Brusini, Perrine Mead, Natasha Boutris, Panagiotis Olawole-Scott, Helen Ahmed, Henna Flanagan, Sheila Mandke, Kanad Keshavarzi, Mahmoud Goswami, Usha |
author_sort | Gibbon, Samuel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8358977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83589772021-09-01 Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG Gibbon, Samuel Attaheri, Adam Ní Choisdealbha, Áine Rocha, Sinead Brusini, Perrine Mead, Natasha Boutris, Panagiotis Olawole-Scott, Helen Ahmed, Henna Flanagan, Sheila Mandke, Kanad Keshavarzi, Mahmoud Goswami, Usha Brain Lang Article 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. Elsevier 2021-09 /pmc/articles/PMC8358977/ /pubmed/34111684 http://dx.doi.org/10.1016/j.bandl.2021.104968 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gibbon, Samuel Attaheri, Adam Ní Choisdealbha, Áine Rocha, Sinead Brusini, Perrine Mead, Natasha Boutris, Panagiotis Olawole-Scott, Helen Ahmed, Henna Flanagan, Sheila Mandke, Kanad Keshavarzi, Mahmoud Goswami, Usha Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title | Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title_full | Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title_fullStr | Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title_full_unstemmed | Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title_short | Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG |
title_sort | machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358977/ https://www.ncbi.nlm.nih.gov/pubmed/34111684 http://dx.doi.org/10.1016/j.bandl.2021.104968 |
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