Linked Life Data

18368141

Source:http://linkedlifedata.com/resource/pubmed/id/18368141

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-3-27
pubmed:abstractText
As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1687-5265
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
94397
pubmed:year
2007
pubmed:articleTitle
A semisupervised support vector machines algorithm for BCI systems.
pubmed:affiliation
Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China. jz.qin@siat.ac.cn
pubmed:publicationType
Journal Article