Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task

Bowen Li; Zhiwen Liu; Xiaorong Gao; Yanfei Lin

doi:10.1145/3383783.3383811

Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task

Bowen Li, Zhiwen Liu, Xiaorong Gao, Yanfei Lin

School of Integrated Circuits and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study will explore an algorithm of spatio-temporal pattern analysis for electroencephalographic (EEG) classification in the rapid serial visual presentation (RSVP) task. In this algorithm, the spatial low-rank and temporal-frequency sparse priors are exploited to train the supervised spatial and temporal filters. The discriminant features are extracted by the supervised spatio-temporal filters and classified by support vector machine. The EEG signals were recorded from a total of 12 subjects under RSVP task and were used as training and testing data. The average true positive rate of classification is 79%, and the average false positive rate is only 3.4%. The classification results show that the proposed algorithm has better performance in the target detection than HDCA and SWFP.

Original language	English
Title of host publication	ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications
Publisher	Association for Computing Machinery
Pages	91-95
Number of pages	5
ISBN (Electronic)	9781450372183
DOIs	https://doi.org/10.1145/3383783.3383811
Publication status	Published - 19 Dec 2019
Event	6th International Conference on Bioinformatics Research and Applications, ICBRA 2019 - Seoul, Korea, Republic of Duration: 19 Dec 2019 → 21 Dec 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Bioinformatics Research and Applications, ICBRA 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	19/12/19 → 21/12/19

Keywords

Discriminant features
EEG
Low-rank
Sparse prior
Spatio-temporal pattern

Access to Document

10.1145/3383783.3383811

Cite this

Li, B., Liu, Z., Gao, X., & Lin, Y. (2019). Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task. In ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications (pp. 91-95). Article 3383811 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3383783.3383811

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title = "Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task",

abstract = "This study will explore an algorithm of spatio-temporal pattern analysis for electroencephalographic (EEG) classification in the rapid serial visual presentation (RSVP) task. In this algorithm, the spatial low-rank and temporal-frequency sparse priors are exploited to train the supervised spatial and temporal filters. The discriminant features are extracted by the supervised spatio-temporal filters and classified by support vector machine. The EEG signals were recorded from a total of 12 subjects under RSVP task and were used as training and testing data. The average true positive rate of classification is 79%, and the average false positive rate is only 3.4%. The classification results show that the proposed algorithm has better performance in the target detection than HDCA and SWFP.",

keywords = "Discriminant features, EEG, Low-rank, Sparse prior, Spatio-temporal pattern",

author = "Bowen Li and Zhiwen Liu and Xiaorong Gao and Yanfei Lin",

note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 6th International Conference on Bioinformatics Research and Applications, ICBRA 2019 ; Conference date: 19-12-2019 Through 21-12-2019",

year = "2019",

month = dec,

day = "19",

doi = "10.1145/3383783.3383811",

language = "English",

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Li, B, Liu, Z, Gao, X & Lin, Y 2019, Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task. in ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications., 3383811, ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 91-95, 6th International Conference on Bioinformatics Research and Applications, ICBRA 2019, Seoul, Korea, Republic of, 19/12/19. https://doi.org/10.1145/3383783.3383811

Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task. / Li, Bowen; Liu, Zhiwen; Gao, Xiaorong et al.
ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications. Association for Computing Machinery, 2019. p. 91-95 3383811 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task

AU - Li, Bowen

AU - Liu, Zhiwen

AU - Gao, Xiaorong

AU - Lin, Yanfei

PY - 2019/12/19

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N2 - This study will explore an algorithm of spatio-temporal pattern analysis for electroencephalographic (EEG) classification in the rapid serial visual presentation (RSVP) task. In this algorithm, the spatial low-rank and temporal-frequency sparse priors are exploited to train the supervised spatial and temporal filters. The discriminant features are extracted by the supervised spatio-temporal filters and classified by support vector machine. The EEG signals were recorded from a total of 12 subjects under RSVP task and were used as training and testing data. The average true positive rate of classification is 79%, and the average false positive rate is only 3.4%. The classification results show that the proposed algorithm has better performance in the target detection than HDCA and SWFP.

AB - This study will explore an algorithm of spatio-temporal pattern analysis for electroencephalographic (EEG) classification in the rapid serial visual presentation (RSVP) task. In this algorithm, the spatial low-rank and temporal-frequency sparse priors are exploited to train the supervised spatial and temporal filters. The discriminant features are extracted by the supervised spatio-temporal filters and classified by support vector machine. The EEG signals were recorded from a total of 12 subjects under RSVP task and were used as training and testing data. The average true positive rate of classification is 79%, and the average false positive rate is only 3.4%. The classification results show that the proposed algorithm has better performance in the target detection than HDCA and SWFP.

KW - Discriminant features

KW - EEG

KW - Low-rank

KW - Sparse prior

KW - Spatio-temporal pattern

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M3 - Conference contribution

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EP - 95

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Li B, Liu Z, Gao X, Lin Y. Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task. In ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications. Association for Computing Machinery. 2019. p. 91-95. 3383811. (ACM International Conference Proceeding Series). doi: 10.1145/3383783.3383811

Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task

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