Classification for Single-Trial N170 During Responding to Facial Picture With Emotion - Tian, Yin; Zhang, Huiling; Pang, Yu; Lin, Jinzhao (Frontiers in Computational Neuroscience, 2018)
Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, while a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive emotion and negative emotion of around 170ms at the occipitotemporal electrodes (i.e. N170). Then, we further proposed applying the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERP was obtained by averaging most of the time while ignoring the trial-to-trial variation. In order to find an optimal classifier for emotional classification with single-trial N170 as feature, three types of classifiers, namely linear discriminant analysis (LDA), L1-regularized logistic regression (L1LR) and support vector machine with radial basis function (RBF-SVM), were comparatively investigated. The results showed that the single-trial N170 could be used as a classification feature to successfully distinguish positive emotion from negative emotion. L1LR classifiers showed a good generalization while LDA showed relatively poor generalization. Moreover, compared to L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle to be applied to the online operating system of brain computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expressions, and that single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain.