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Analysis and Classification of Evoked Potentials in Response to Familiar and Unfamiliar Faces

机译:对熟悉和陌生面孔的诱发电位的分析和分类

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Brain activity during perception and recognition of faces have been studied by researchers with the purpose to develop brain-computer interfaces and to study neurological disorders. In this paper, we analyzed evoked potentials as neurophysiological indicators and developed a model based on signal processing and machine learning techniques to find descriptive patterns that allow the differentiation of familiar and unfamiliar faces. We considered wave components such as P1, N170, N250, P300, and N400 to describe the events. Morphological analysis and wavelet transform were used for the feature extraction stage, and support vector machines and binomial logistic regression were evaluated for the classification stage. The best classification results were obtained with the morphological characteristics, where the highest classification accuracy was 80% on average.
机译:研究人员已经研究了在感知和识别脸部过程中的大脑活动,目的是开发脑机接口和研究神经系统疾病。在本文中,我们分析了诱发电位作为神经生理指标,并基于信号处理和机器学习技术开发了一个模型,以找到描述性模式,以区分熟悉和不熟悉的面孔。我们考虑了诸如P1,N170,N250,P300和N400等波分量来描述这些事件。在特征提取阶段使用了形态分析和小波变换,在分类阶段评估了支持向量机和二项式逻辑回归。具有形态特征的分类结果最好,其中最高分类准确率平均为80%。

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