Window function for EEG power density estimation and its application in SSVEP based BCIs

Abstract

A high quality power density estimation for certain frequency components in a short time is of key importance in Steady-State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI). In this paper, the effect of the window functions in SSVEP based BCIs is discussed. EEG signal is a typical color noise with a high energy of the low frequency component. The main findings are that (1) The spectral leakage for EEG signals has some regular patterns. An obvious oscillation with the corresponding frequency can be observed. The amplitude of the oscillation decreases with the growth of the frequency. A short analysis is also given for the leakage. (2) The leakage from the low frequency component can be effectively suppressed by the using of some windows, such as Hamming, Hann and triangle window; (3) By removing the influence of the leakage from the low frequency component with high pass filter, the classification results are mainly determined by the width of the main lobe. The rectangle window would have a better accuracy than Hamming, Hann and triangle window. Some windows constructed with a narrower main lobe width have a potential use in SSVEP based BCIs.

Gan Huang

My research interests include Neural Modulation, Brain Computer Interface and Neural Prosthetics.