Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification
The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal...
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| Format: | Article |
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Frontiers Media S.A.
2017-11-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fncom.2017.00106/full |
| _version_ | 1818138167229808640 |
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| author | Lei Jiang Lei Jiang Yun Wang Yun Wang Bangyu Cai Bangyu Cai Yueming Wang Yueming Wang Yiwen Wang |
| author_facet | Lei Jiang Lei Jiang Yun Wang Yun Wang Bangyu Cai Bangyu Cai Yueming Wang Yueming Wang Yiwen Wang |
| author_sort | Lei Jiang |
| collection | DOAJ |
| description | The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification. |
| first_indexed | 2024-12-11T10:07:53Z |
| format | Article |
| id | doaj.art-b0dd9849983844bfbab597d48782917f |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-11T10:07:53Z |
| publishDate | 2017-11-01 |
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| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-b0dd9849983844bfbab597d48782917f2022-12-22T01:11:52ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882017-11-011110.3389/fncom.2017.00106290696Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face IdentificationLei Jiang0Lei Jiang1Yun Wang2Yun Wang3Bangyu Cai4Bangyu Cai5Yueming Wang6Yueming Wang7Yiwen Wang8Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Biomedical Engineering, Zhejiang University, Hangzhou, ChinaQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, ChinaDepartment of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaDepartment of Electronic and Computer Engineering, Department of Chemical and Biology Engineering, Hong Kong University of Science and Technology, Kowloon, Hong KongThe event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification.http://journal.frontiersin.org/article/10.3389/fncom.2017.00106/fullevent related potentialsingle-trialspatial-temporal featurebrain-computer interfacerapid face identification |
| spellingShingle | Lei Jiang Lei Jiang Yun Wang Yun Wang Bangyu Cai Bangyu Cai Yueming Wang Yueming Wang Yiwen Wang Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification Frontiers in Computational Neuroscience event related potential single-trial spatial-temporal feature brain-computer interface rapid face identification |
| title | Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification |
| title_full | Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification |
| title_fullStr | Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification |
| title_full_unstemmed | Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification |
| title_short | Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification |
| title_sort | spatial temporal feature analysis on single trial event related potential for rapid face identification |
| topic | event related potential single-trial spatial-temporal feature brain-computer interface rapid face identification |
| url | http://journal.frontiersin.org/article/10.3389/fncom.2017.00106/full |
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