Author/Authors :
Sheykhivand, S. Faculty of Electrical and Computer Engineering - University of Tabriz, Tabriz, Iran , Yousefi Rezaii, T. Faculty of Electrical and Computer Engineering - University of Tabriz, Tabriz, Iran , Naderi Saatlo, A. Department of Electrical-Electronics Engineering - Urmia Branch, Islamic Azad University, Urmia, Iran , Romooz, N. Department of Electrical-Electronics Engineering - Urmia Branch, Islamic Azad University, Urmia, Iran
Abstract :
There are different feature extraction methods in brain-computer interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) systems. This paper presents a comparison of five methods for stimulation frequency detection in SSVEP-based BCI systems. The techniques are based on Power Spectrum Density Analysis (PSDA), Fast Fourier Transform (FFT), Hilbert- Huang Transform (HHT), Cross Correlation and Canonical Correlation Analysis (CCA). The results demonstrate that the CCA and FFT can be successfully applied for stimulus frequency detection by considering the highest accuracy and minimum consuming time.
Keywords :
BCI , CCA , SSVEP , Cross Correlation , FFT , Fuzzy , HHT , PSDA
