Comparison Between Different Methods of Feature Extraction in BCI Systems Based on SSVEP

محورهای موضوعی : مجله بین المللی ریاضیات صنعتی

S. ‎Sheykhivand‎ ¹ , T. ‎Yousefi ‎R‎ezaii‎ ² , A. ‎Naderi Saatlo‎ ³ , N. ‎Romooz‎ ⁴

1 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
2 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
3 - Department of Electrical-Electronics Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
4 - Department of Electrical-Electronics Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.

تاریخ دریافت : 1396/12/10 تاریخ پذیرش : 1396/12/10 تاریخ انتشار : 1396/06/10

کلید واژه: BCI, CCA, Cross Correlation, FFT, fuzzy, HHT, PSDA, &amp, lrm, SSVEP&amp, lrm, ,

چکیده مقاله :

‎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.‎