Several techniques have been used to improve the signal-to-noise ratio to increase the detection rate of Event Related Potentials (ERPs). This work investigates the application of spatial filtering based on principal component analysis (PCA) to detect ERP due to left-hand index finger movement imagination. The EEG signals were recorded of central derivations (C4, C2, Cz, C1 and C3), positioned according to 10-10 International System. The optimal spatial filter was found by using the first principal component and the ERP detection was obtained by magnitude squared coherence technique. The best detection rate, by using original signal (without filtering), was obtained at C2 derivation, with 54.73% for significance level of 5%. For the same significance level, the detection rate of the filtered signal was drastically improved to 96.84%. Results suggest that spatial filter by using PCA might be a very useful tool in assisting the ERP detection for movement imagination for applications on brain machine interface.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.