Feature Extraction of EEG Signals Using Power Spectral Entropy

Abstract

Brain-Computer Interfaces (BCI) use electroencephalography (EEG) signals recorded from the scalp to create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. One of the most important components of BCI is feature extraction of EEG signals. How to rapidly and reliably extract EEG features for expressing the brain states of different mental tasks is the crucial element for exact classification. This paper presents an approach that performs EEG feature extraction during imagined right and left hand movements by using power spectral entropy (PSE). It acquires good classification results with the time-variable linear classifier. The maximal accuracy achieves 90%. The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements. The method is simple and quick and it provides a promising method for on-line BCI system.