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Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

文献类型：期刊论文

作者Zhang, Chuncheng2,3; Qiu, Shuang2,3; Wang, Shengpei2,3; He, Huiguang1,2,3,4
刊名FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
出版日期2021-02-26
卷号15页码:13
关键词RSVP ERP MEG CNN SVM
DOI10.3389/fncom.2021.619508
通讯作者He, Huiguang(huiguang.he@ia.ac.cn)
英文摘要Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain-computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm. Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target. Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment. Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.
资助项目National Natural Science Foundation of China[61976209] ; National Natural Science Foundation of China[62020106015] ; National Natural Science Foundation of China[61906188] ; Chinese Academy of Sciences (CAS) International Collaboration Key Project[173211KYSB20190024] ; Strategic Priority Research Program of CAS[XDA27000000] ; National Nature Science Foundation of China[31730039]
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000627769800001
出版者FRONTIERS MEDIA SA
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) International Collaboration Key Project ; Strategic Priority Research Program of CAS ; National Nature Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/44088]  
专题类脑智能研究中心_神经计算及脑机交互
通讯作者He, Huiguang
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Chuncheng,Qiu, Shuang,Wang, Shengpei,et al. Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2021,15:13.
APA Zhang, Chuncheng,Qiu, Shuang,Wang, Shengpei,&He, Huiguang.(2021).Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,15,13.
MLA Zhang, Chuncheng,et al."Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 15(2021):13.

入库方式： OAI收割

来源：自动化研究所

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