Comparison of Classifiers and Statistical Analysis for EEG Signals Used in Brain Computer Interface Motor Task Paradigm
Journal Title: International Journal of Advanced Research in Artificial Intelligence(IJARAI) - Year 2015, Vol 4, Issue 1
Abstract
Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI) paradigm. The purpose of the quantitative research is to compare classifiers in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluated the (de)synchronizations that appear on Mu rhythm. The features extracted from EEG signals are classified using linear discriminant classifier (LDA), quadratic classifier (QDA) and classifier based on Mahalanobis distance (MD). The differences between LDA, QDA and MD are small, but the superiority of QDA was sustained by analysis of variance (ANOVA).
Authors and Affiliations
Oana Eva, Anca Lazar
Keywords
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- EP ID EP89126
- DOI 10.14569/IJARAI.2015.040102
- Views 126
- Downloads 0
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