A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions

Unlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI) has not been established as a nonverbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but indi...

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Main Author: Koji eKashihara
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-08-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00244/full
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spelling doaj-ced9e6386b0746348c3f67e5d848a0132020-11-24T22:52:52ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-08-01810.3389/fnins.2014.0024479977A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific EmotionsKoji eKashihara0The University of TokushimaUnlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI) has not been established as a nonverbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients’ emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based nonverbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600–700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus. This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00244/fullsource localizationface recognitionBrain computer interfaces (BCI)Aversive Conditioningneutral faces
collection DOAJ
language English
format Article
sources DOAJ
author Koji eKashihara
spellingShingle Koji eKashihara
A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
Frontiers in Neuroscience
source localization
face recognition
Brain computer interfaces (BCI)
Aversive Conditioning
neutral faces
author_facet Koji eKashihara
author_sort Koji eKashihara
title A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
title_short A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
title_full A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
title_fullStr A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
title_full_unstemmed A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions
title_sort brain–computer interface for potential nonverbal facial communication based on eeg signals related to specific emotions
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2014-08-01
description Unlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI) has not been established as a nonverbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients’ emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based nonverbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600–700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus. This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals.
topic source localization
face recognition
Brain computer interfaces (BCI)
Aversive Conditioning
neutral faces
url http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00244/full
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