Classification for Single-Trial N170 During Responding to Facial Picture With Emotion.
Classification for Single-Trial N170 During Responding to Facial Picture With Emotion.
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单次试验 N170 在对面部图片进行情感反应时的分类
DOI：
10.3389/fncom.2018.00068
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发表时间：
2018
影响因子：
3.2
通讯作者：
Lin J
中科院分区：
文献类型：
--
作者：
Tian Y;Zhang H;Pang Y;Lin J
与面部识别相关的事件相关电位(ERP)N170是否受情绪调节一直是一个有争议的问题。一些研究人员认为N170与情绪无关，而最近的一项研究显示了相反的观点。在目前的研究中，利用情绪反应面部图片时的脑电(EEG)记录来研究N170是否受到情绪的调节。我们发现，在枕颞部电极(即N170)上，积极情绪和消极情绪在170ms左右的ERP试验之间存在显著差异。然后，我们进一步提出将单次试验N170作为面部情绪分类的特征，从而避免了大多数情况下ERPs是通过平均来获得的，而忽略了试验之间的差异。为了寻找一种最优的以N170为特征的情感分类分类器，对线性判别分析(LDA)、L1正则化Logistic回归(L1LR)和径向基支持向量机(RBF-SVM)三种分类器进行了比较研究。结果表明，N170可以作为一种分类特征来成功区分积极情绪和消极情绪。L1正则化Logistic回归分类器具有较好的泛化能力，而LDA泛化能力较差。此外，与L1LR相比，径向基函数支持向量机在分类过程中需要更多的时间来优化参数，这成为其应用于脑机接口(BCI)在线操作系统的障碍。这些发现表明，面部相关的N170可能会受到面部表情的影响，单次试验的N170可能是一个生物标记物，用于监测BCI领域受试者的情绪状态。
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, whereas 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 and negative emotions of around 170 ms at the occipitotemporal electrodes (i.e., N170). Then, we further proposed the application of the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERPs were 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 a 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. L1-regularized logistic regression classifiers showed a good generalization, whereas LDA showed a relatively poor generalization. Moreover, when compared with L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle while applying it to the online operating system of brain-computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expression and that the single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain.
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影响因子：
8.9
作者：
Huang, J;Ling, CX
通讯作者：
Ling, CX
DOI：
10.1109/jstsp.2007.910971
发表时间：
2007-12-01
影响因子：
7.5
作者：
Kim, Seung-Jean;Koh, K.;Gorinevsky, Dimitry
通讯作者：
Gorinevsky, Dimitry
DOI：
10.2174/1874440001206010026
发表时间：
2012
期刊：
The open neuroimaging journal
影响因子：
--
作者：
Fujimoto T;Okumura E;Takeuchi K;Kodabashi A;Tanaka H;Otsubo T;Nakamura K;Sekine M;Kamiya S;Higashi Y;Tsuji M;Shimooki S;Tamura T
通讯作者：
Tamura T
与事件相关的大脑潜在情绪处理的相关性。
影响因子：
2.6
作者：
Eimer, Martin;Holmes, Amanda
通讯作者：
Holmes, Amanda
影响因子：
19.9
作者：
Lindquist, Kristen A.;Barrett, Lisa Feldman
通讯作者：
Barrett, Lisa Feldman