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Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography
Schizotypy refers to the personality trait of experiencing “psychotic” symptoms and can be regarded as a predisposition of schizophrenia-spectrum psychopathology (Raine, 1991). Cumulative evidence has revealed that individuals with schizotypy, as well as schizophrenia patients, have emotional proces...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601065/ https://www.ncbi.nlm.nih.gov/pubmed/28955212 http://dx.doi.org/10.3389/fnhum.2017.00450 |
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author | Jeong, Ji Woon Wendimagegn, Tariku W. Chang, Eunhee Chun, Yeseul Park, Joon Hyuk Kim, Hyoung Joong Kim, Hyun Taek |
author_facet | Jeong, Ji Woon Wendimagegn, Tariku W. Chang, Eunhee Chun, Yeseul Park, Joon Hyuk Kim, Hyoung Joong Kim, Hyun Taek |
author_sort | Jeong, Ji Woon |
collection | PubMed |
description | Schizotypy refers to the personality trait of experiencing “psychotic” symptoms and can be regarded as a predisposition of schizophrenia-spectrum psychopathology (Raine, 1991). Cumulative evidence has revealed that individuals with schizotypy, as well as schizophrenia patients, have emotional processing deficits. In the present study, we investigated multimodal emotion perception in schizotypy and implemented the machine learning technique to find out whether a schizotypy group (ST) is distinguishable from a control group (NC), using electroencephalogram (EEG) signals. Forty-five subjects (30 ST and 15 NC) were divided into two groups based on their scores on a Schizotypal Personality Questionnaire. All participants performed an audiovisual emotion perception test while EEG was recorded. After the preprocessing stage, the discriminatory features were extracted using a mean subsampling technique. For an accurate estimation of covariance matrices, the shrinkage linear discriminant algorithm was used. The classification attained over 98% accuracy and zero rate of false-positive results. This method may have important clinical implications in discriminating those among the general population who have a subtle risk for schizotypy, requiring intervention in advance. |
format | Online Article Text |
id | pubmed-5601065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56010652017-09-27 Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography Jeong, Ji Woon Wendimagegn, Tariku W. Chang, Eunhee Chun, Yeseul Park, Joon Hyuk Kim, Hyoung Joong Kim, Hyun Taek Front Hum Neurosci Neuroscience Schizotypy refers to the personality trait of experiencing “psychotic” symptoms and can be regarded as a predisposition of schizophrenia-spectrum psychopathology (Raine, 1991). Cumulative evidence has revealed that individuals with schizotypy, as well as schizophrenia patients, have emotional processing deficits. In the present study, we investigated multimodal emotion perception in schizotypy and implemented the machine learning technique to find out whether a schizotypy group (ST) is distinguishable from a control group (NC), using electroencephalogram (EEG) signals. Forty-five subjects (30 ST and 15 NC) were divided into two groups based on their scores on a Schizotypal Personality Questionnaire. All participants performed an audiovisual emotion perception test while EEG was recorded. After the preprocessing stage, the discriminatory features were extracted using a mean subsampling technique. For an accurate estimation of covariance matrices, the shrinkage linear discriminant algorithm was used. The classification attained over 98% accuracy and zero rate of false-positive results. This method may have important clinical implications in discriminating those among the general population who have a subtle risk for schizotypy, requiring intervention in advance. Frontiers Media S.A. 2017-09-12 /pmc/articles/PMC5601065/ /pubmed/28955212 http://dx.doi.org/10.3389/fnhum.2017.00450 Text en Copyright © 2017 Jeong, Wendimagegn, Chang, Chun, Park, Kim and Kim. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jeong, Ji Woon Wendimagegn, Tariku W. Chang, Eunhee Chun, Yeseul Park, Joon Hyuk Kim, Hyoung Joong Kim, Hyun Taek Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title | Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title_full | Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title_fullStr | Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title_full_unstemmed | Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title_short | Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography |
title_sort | classifying schizotypy using an audiovisual emotion perception test and scalp electroencephalography |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601065/ https://www.ncbi.nlm.nih.gov/pubmed/28955212 http://dx.doi.org/10.3389/fnhum.2017.00450 |
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