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

doi:10.3389/fncom.2021.619508

	Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data
	Zhang, Chuncheng 1,2; Qiu, Shuang1,2 ; Wang, Shengpei1,2 ; He, Huiguang1,2,3,4
发表期刊	FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
	2021-02-26
卷号	15 页码:13
通讯作者	He, Huiguang(huiguang.he@ia.ac.cn)
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.
关键词	RSVP ERP MEG CNN SVM
DOI	10.3389/fncom.2021.619508
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000627769800001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	脑机接口
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44089
专题	脑图谱与类脑智能实验室_神经计算与脑机交互
通讯作者	He, Huiguang
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
第一作者单位	模式识别国家重点实验室; 类脑智能研究中心
通讯作者单位	模式识别国家重点实验室; 类脑智能研究中心
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.