Optimum principal components for spatial filtering of EEG to detect imaginary movement by coherence

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.