A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.
A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.
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DOI：
10.3389/fnins.2014.00244
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发表时间：
2014
影响因子：
4.3
通讯作者：
Kashihara K
中科院分区：
文献类型：
--
作者：
Kashihara K
与语言交流的辅助技术不同，脑机或脑机接口（BMI/BCI）尚未被确立为肌萎缩侧索硬化症（ALS）患者的非语言交流工具。面对面的交流可以获得丰富的情感信息，但患有神经系统疾病的个体，如ALS和自闭症，可能不会表达他们的情绪或交流他们的负面感受。虽然情绪可以通过观察面部表情推断出来，但对中性面孔的情绪预测需要提前判断。大脑神经元对中性面孔的反应并导致情绪变化的过程尚不清楚。因此，为了解决这个问题，本研究试图解码对中性面部刺激的条件情绪反应。这个方向的动机是假设脑电图（EEG）信号可以用来检测患者对特定无表情面孔的情绪反应，结果可以纳入基于BMI/ bci的非语言交流工具的设计和开发。为此，本研究调查了与负面情绪相关的中性面孔如何调节面部加工的快速中枢反应，然后确定了皮层活动。来自后颞叶的条件中性面孔触发事件相关电位在刺激后的后期（600-700 ms）发生显著变化，而在早期面孔加工活动（如P1和N170反应）中发生显著变化。源定位显示，条件中性面在右侧梭状回（FG）的活动增加。本研究还开发了一种利用脑电图信号检测特定面孔的内隐负面情绪反应的有效方法。一种基于支持向量机的分类方法可以很容易地对触发特定个人情绪的中性面孔进行分类。根据这种分类，电脑上的脸会变成悲伤或不高兴的表情。所提出的方法可以作为非语言交流工具的一部分来实现情感表达。
Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal 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 non-verbal 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 (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In accordance with this classification, a face on a computer morphs into a sad or displeased countenance. The proposed method could be incorporated as a part of non-verbal communication tools to enable emotional expression.
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