EEG-based classification of natural sounds reveals specialized responses to speech and music

Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger an...

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Main Authors: Nathaniel J. Zuk, Emily S. Teoh, Edmund C. Lalor
Format: Article
Language:English
Published: Elsevier 2020-04-01
Series:NeuroImage
Subjects:
EEG
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920300458
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spelling doaj-74d6e883a47a4bc5b35af12e2e46310e2020-11-25T03:44:34ZengElsevierNeuroImage1095-95722020-04-01210116558EEG-based classification of natural sounds reveals specialized responses to speech and musicNathaniel J. Zuk0Emily S. Teoh1Edmund C. Lalor2Department of Electronic & Electrical Engineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Corresponding author.Department of Electronic & Electrical Engineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin 2, IrelandDepartment of Electronic & Electrical Engineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland; Department of Biomedical Engineering, University of Rochester, New York 14627, USA; Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester Medical Center, University of Rochester, New York 14627, USAHumans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music compared to other types of sounds, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical frequency and modulation statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the patterns in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective, temporally precise, and efficient way to study the human brain’s selectivity for speech and music in other populations.http://www.sciencedirect.com/science/article/pii/S1053811920300458EEGNatural soundsBiophysical modelClassification analysisSpeechMusic
collection DOAJ
language English
format Article
sources DOAJ
author Nathaniel J. Zuk
Emily S. Teoh
Edmund C. Lalor
spellingShingle Nathaniel J. Zuk
Emily S. Teoh
Edmund C. Lalor
EEG-based classification of natural sounds reveals specialized responses to speech and music
NeuroImage
EEG
Natural sounds
Biophysical model
Classification analysis
Speech
Music
author_facet Nathaniel J. Zuk
Emily S. Teoh
Edmund C. Lalor
author_sort Nathaniel J. Zuk
title EEG-based classification of natural sounds reveals specialized responses to speech and music
title_short EEG-based classification of natural sounds reveals specialized responses to speech and music
title_full EEG-based classification of natural sounds reveals specialized responses to speech and music
title_fullStr EEG-based classification of natural sounds reveals specialized responses to speech and music
title_full_unstemmed EEG-based classification of natural sounds reveals specialized responses to speech and music
title_sort eeg-based classification of natural sounds reveals specialized responses to speech and music
publisher Elsevier
series NeuroImage
issn 1095-9572
publishDate 2020-04-01
description Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music compared to other types of sounds, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical frequency and modulation statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the patterns in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective, temporally precise, and efficient way to study the human brain’s selectivity for speech and music in other populations.
topic EEG
Natural sounds
Biophysical model
Classification analysis
Speech
Music
url http://www.sciencedirect.com/science/article/pii/S1053811920300458
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