Classification of single trial EEG during imagined hand movement by rhythmic component extraction

An electroencephalograph (EEG)-based brain computer interface (BCI) requires rapid and reliable extraction of features in EEG signal. Recently, the rhythmic component extraction (RCE) method has been proposed to extract features of multi-channel EEG. RCE can extract a signal component with a certain frequency from multi-sensor signals. In this paper, we applied RCE to extract a feature corresponding to hand movement imagery tasks from signals measured by EEG. This feature from a single trial EEG signal is classified between imaginary left/right hand movement EEG using machine learning. On two subjects, our experiment shows that the combination of RCE and fisher discriminant analysis outperforms common spatial patterns (CSP) in classification accuracy. It is also reported that other major classifiers together with RCE give better performance than CSP. Additionally, we consider the relationship between data length and classification accuracy. It is shown that the accuracy tends to decrease as the data length becomes small.