Phase space reconstruction for improvement of classification in few-channel BCI systems

With advances in brain-computer interface (BCI) research for the practical use of BCI systems, few-channel BCI systems have become necessary. The common spatial pattern (CSP) algorithm is a classic and powerful tool for extraction of features for motor imagery in BCI systems. However, previous studies show that this algorithm is not suitable for few-channel systems. In this study, phase space reconstruction (PSR) was used to decompose few-channel electroencephalography (EEG) signals into multichannel information. Using the reconstructed data, CSP and a support vector machine (SVM) were combined to obtain high classification accuracies from a small number of channels. The mean accuracy for the EEG signals from three channels was 0.74 for PSR + CSP + SVM, while this accuracy was only 0.43 for CSP + SVM, which suggests that PSR + CSP + SVM is practicable for few-channel BCI systems.