Single-trial-based temporal principal component analysis on extracting event-related potentials of interest for an individual subject
Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for...
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| Vydáno v: | Journal of neuroscience methods Ročník 385; s. 109768 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.02.2023
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| ISSN: | 0165-0270, 1872-678X, 1872-678X |
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| Abstract | Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for one ERP vary considerably across participants. Furthermore, effect of number of trials on tPCA decomposition has not been systematically examined as well, especially for within-subject PCA.
We reanalyzed a real ERP data of an emotional experiment using tPCA to extract N2 and P2 from single-trial EEG of an individual. We also explored influence of the number of trials (consecutively increased from 10 to 42 trials) on PCA decomposition by comparing temporal correlation, the statistical result, Cronbach’s alpha, spatial correlation of both N2 and P2 for the proposed method with the conventional time-domain analysis, trial-averaged group PCA, and single-trial-based group PCA.
The results of the proposed method can enhance spatial and temporal consistency. We could obtain stable N2 with few trials (about 20) for the proposed method, but, for P2, approximately 30 trials were needed for all methods.
About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods.
The proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis. |
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| AbstractList | Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for one ERP vary considerably across participants. Furthermore, effect of number of trials on tPCA decomposition has not been systematically examined as well, especially for within-subject PCA.BACKGROUNDTemporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for one ERP vary considerably across participants. Furthermore, effect of number of trials on tPCA decomposition has not been systematically examined as well, especially for within-subject PCA.We reanalyzed a real ERP data of an emotional experiment using tPCA to extract N2 and P2 from single-trial EEG of an individual. We also explored influence of the number of trials (consecutively increased from 10 to 42 trials) on PCA decomposition by comparing temporal correlation, the statistical result, Cronbach's alpha, spatial correlation of both N2 and P2 for the proposed method with the conventional time-domain analysis, trial-averaged group PCA, and single-trial-based group PCA.NEW METHODWe reanalyzed a real ERP data of an emotional experiment using tPCA to extract N2 and P2 from single-trial EEG of an individual. We also explored influence of the number of trials (consecutively increased from 10 to 42 trials) on PCA decomposition by comparing temporal correlation, the statistical result, Cronbach's alpha, spatial correlation of both N2 and P2 for the proposed method with the conventional time-domain analysis, trial-averaged group PCA, and single-trial-based group PCA.The results of the proposed method can enhance spatial and temporal consistency. We could obtain stable N2 with few trials (about 20) for the proposed method, but, for P2, approximately 30 trials were needed for all methods.RESULTSThe results of the proposed method can enhance spatial and temporal consistency. We could obtain stable N2 with few trials (about 20) for the proposed method, but, for P2, approximately 30 trials were needed for all methods.About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods.COMPARISON WITH EXISTING METHOD(S)About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods.The proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis.CONCLUSIONThe proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis. Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for one ERP vary considerably across participants. Furthermore, effect of number of trials on tPCA decomposition has not been systematically examined as well, especially for within-subject PCA. We reanalyzed a real ERP data of an emotional experiment using tPCA to extract N2 and P2 from single-trial EEG of an individual. We also explored influence of the number of trials (consecutively increased from 10 to 42 trials) on PCA decomposition by comparing temporal correlation, the statistical result, Cronbach’s alpha, spatial correlation of both N2 and P2 for the proposed method with the conventional time-domain analysis, trial-averaged group PCA, and single-trial-based group PCA. The results of the proposed method can enhance spatial and temporal consistency. We could obtain stable N2 with few trials (about 20) for the proposed method, but, for P2, approximately 30 trials were needed for all methods. About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods. The proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis. |
| ArticleNumber | 109768 |
| Author | Lu, Yingzhi Zhang, Guanghui Li, Xueyan Tiihonen, Timo Chang, Zheng Cong, Fengyu |
| Author_xml | – sequence: 1 givenname: Guanghui surname: Zhang fullname: Zhang, Guanghui email: zhang.guanghui@foxmail.com organization: School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China – sequence: 2 givenname: Xueyan surname: Li fullname: Li, Xueyan organization: School of Foreign Languages, Dalian University of Technology, Dalian, 116024, China – sequence: 3 givenname: Yingzhi surname: Lu fullname: Lu, Yingzhi organization: School of Psychology, Shanghai University of Sport, Shanghai, 200438, China – sequence: 4 givenname: Timo surname: Tiihonen fullname: Tiihonen, Timo organization: Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland – sequence: 5 givenname: Zheng surname: Chang fullname: Chang, Zheng organization: Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland – sequence: 6 givenname: Fengyu surname: Cong fullname: Cong, Fengyu email: cong@dlut.edu.cn organization: School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36529386$$D View this record in MEDLINE/PubMed |
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| Keywords | Individual subject Event-related potentials Back-projection Single-trial analysis Principal component analysis |
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