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- W2360147143 abstract "In order to solve the problem that how to make full use of the unlabeled samples to promote recognition accuracy due to high cost acquisition of data in BCI research,biomimetic pattern recognition(BPR) was combined with semi-supervised learning algorithm on the basis of the Bagging algorithm framework,and a novel Incremental Semi-Supervised Biomimetic Pattern Recognition(BPR-ISSL) was proposed in this work.We conducted off-line analysis and simulative on-line experiments on the datasets from previous brain-computer interface competitions and real on-line experiments on actual on-line EEG data to test the accuracy and efficiency of the proposed algorithm.Comparing with those of improved BPR,SVM-ISSL and BP-ISSL,the BPR-ISSL showed that higher accuracy(3% higher in average) in reference to those of the other three algorithms,especially with the sufficient samples.Furthermore,it has a better anti-over-learning ability and stability according to standard deviation." @default.
- W2360147143 created "2016-06-24" @default.
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- W2360147143 date "2011-01-01" @default.
- W2360147143 modified "2023-09-23" @default.
- W2360147143 title "Motor Imagery EEG Recognition Based on Incremental Semi-supervised Biomimetic Pattern Recognition" @default.
- W2360147143 hasPublicationYear "2011" @default.
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