Adaptive schemes applied to online SVM for BCI data classification

Author

Oskoei, Mohammadreza Asghari ; Gan, John Q. ; Hu, Huosheng

Author_Institution

Sch. of CS & EE, Univ. of Essex, Colchester, UK

fYear

2009

fDate

3-6 Sept. 2009

Firstpage

2600

Lastpage

2603

Abstract

This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate.

Keywords

brain-computer interfaces; electroencephalography; learning (artificial intelligence); medical signal processing; signal classification; support vector machines; BCI data classification; EEG; adaptive scheme; brain-computer interface; online SVM process; supervised scheme; support vector machine; unsupervised scheme; Algorithms; Artificial Intelligence; Brain; Brain Mapping; Computational Biology; Equipment Design; Fuzzy Logic; Humans; Internet; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Signal Processing, Computer-Assisted; Software; User-Computer Interface;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Conference_Location

Minneapolis, MN

ISSN

1557-170X

Print_ISBN

978-1-4244-3296-7

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2009.5335328

Filename

5335328