TY - JOUR
T1 - Improved semisupervised adaptation for a small training dataset in the brain-computer interface
AU - Meng, Jianjun
AU - Sheng, Xinjun
AU - Zhang, Dingguo
AU - Zhu, Xiangyang
PY - 2014/7
Y1 - 2014/7
N2 - One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e.g., a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data.
AB - One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e.g., a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data.
KW - Bayesian linear discriminant analysis (BLDA)
KW - brain-computer interface (BCI)
KW - common spatial pattern (CSP)
KW - linear discriminant analysis (LDA)
KW - semisupervised adaptation
UR - http://www.scopus.com/inward/record.url?scp=84904312608&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904312608&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2013.2285232
DO - 10.1109/JBHI.2013.2285232
M3 - Article
C2 - 24122610
AN - SCOPUS:84904312608
SN - 2168-2194
VL - 18
SP - 1461
EP - 1472
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 4
M1 - 6626634
ER -