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An ERP study on visual attention to facial stimuli; N170 component

机译:对面部刺激视觉注意的ERP研究; N170组件

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Attention to picture of face, particularly the human face is related to complex information processing in the brain. Humans pay more attention to human faces than other images. The purpose of this study is to verify the existence of particular attention to facial images and categorize the difference between attending to facial and non-facial images through a pair of different pictures as the targets. According to effects of visual stimuli such as color and luminance, the pictures modulated in greyscale (luminance-defined stimuli). Using a psychophysical task, EEG signals according to 10-20 standards in eight channels were recorded from 48 healthy volunteers. After the initial processing, ERP signal were elicited into two classes according to attention to the face and non-face images. In this study, the time window of the N170 component, was considered to extract new time features plus the N170 component; a negative peak in 170 milliseconds after stimulus onset. Optimum features were selected by t test criteria and classification was done by LDA, KNN and SVM classifiers. Validating the results was done by LOO cross validation criteria. Best result was obtained by SVM with 74.44% and was associated with frontal and parietal lobes.
机译:注意面部图片,特别是人脸与大脑中复杂的信息处理有关。与其他图像相比,人类对人脸的关注程度更高。这项研究的目的是验证是否存在对面部图像的特别关注,并通过以一对不同的图片为目标,对关注面部和非面部图像之间的区别进行分类。根据视觉刺激(例如颜色和亮度)的影响,图像以灰度(亮度定义的刺激)进行调制。使用心理生理任务,从48位健康志愿者的8个通道中记录了根据10-20标准的EEG信号。初始处理后,根据对人脸和非人脸图像的关注程度,将ERP信号分为两类。在这项研究中,考虑了N170分量的时间窗,以提取新的时间特征以及N170分量。刺激发作后170毫秒内出现一个负峰值。通过t检验标准选择最佳特征,并通过LDA,KNN和SVM分类器进行分类。通过LOO交叉验证标准来验证结果。 SVM的最佳结果为74.44%,与额叶和顶叶相关。

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