TY - GEN
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
N1 - Publisher Copyright:
© 2019 ACM.
PY - 2019/12/19
Y1 - 2019/12/19
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
UR - http://www.scopus.com/inward/record.url?scp=85089266941&partnerID=8YFLogxK
U2 - 10.1145/3383783.3383811
DO - 10.1145/3383783.3383811
M3 - Conference contribution
AN - SCOPUS:85089266941
T3 - ACM International Conference Proceeding Series
SP - 91
EP - 95
BT - ICBRA 2019 - Proceedings of 2019 6th International Conference on Bioinformatics Research and Applications
PB - Association for Computing Machinery
T2 - 6th International Conference on Bioinformatics Research and Applications, ICBRA 2019
Y2 - 19 December 2019 through 21 December 2019
ER -