Title :
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
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2009.5335328
