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Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using moto...

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Autores principales: Pan, Jiahui, Xie, Qiuyou, Huang, Haiyun, He, Yanbin, Sun, Yuping, Yu, Ronghao, Li, Yuanqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962793/
https://www.ncbi.nlm.nih.gov/pubmed/29867421
http://dx.doi.org/10.3389/fnhum.2018.00198
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author Pan, Jiahui
Xie, Qiuyou
Huang, Haiyun
He, Yanbin
Sun, Yuping
Yu, Ronghao
Li, Yuanqing
author_facet Pan, Jiahui
Xie, Qiuyou
Huang, Haiyun
He, Yanbin
Sun, Yuping
Yu, Ronghao
Li, Yuanqing
author_sort Pan, Jiahui
collection PubMed
description For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.
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spelling pubmed-59627932018-06-04 Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System Pan, Jiahui Xie, Qiuyou Huang, Haiyun He, Yanbin Sun, Yuping Yu, Ronghao Li, Yuanqing Front Hum Neurosci Neuroscience For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses. Frontiers Media S.A. 2018-05-15 /pmc/articles/PMC5962793/ /pubmed/29867421 http://dx.doi.org/10.3389/fnhum.2018.00198 Text en Copyright © 2018 Pan, Xie, Huang, He, Sun, Yu and Li. 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) and the copyright owner 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
Pan, Jiahui
Xie, Qiuyou
Huang, Haiyun
He, Yanbin
Sun, Yuping
Yu, Ronghao
Li, Yuanqing
Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title_full Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title_fullStr Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title_full_unstemmed Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title_short Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System
title_sort emotion-related consciousness detection in patients with disorders of consciousness through an eeg-based bci system
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962793/
https://www.ncbi.nlm.nih.gov/pubmed/29867421
http://dx.doi.org/10.3389/fnhum.2018.00198
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