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

Authors

A. ‎Naderi Saatlo‎ Department of Electrical-Electronics Engineering, Urmia Branch‎, ‎Islamic Azad University‎, ‎Urmia‎, ‎Iran.

N. ‎Romooz‎ Department of Electrical-Electronics Engineering, Urmia Branch‎, ‎Islamic Azad University‎, ‎Urmia‎, ‎Iran.

S. ‎Sheykhivand‎ Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

T. ‎Yousefi ‎R‎ezaii‎ Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 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.‎

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Journal title

International Journal of Industrial Mathematics

volume 9 issue 4

pages 341- 347

publication date 2017-09-01

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Keywords

BCI CCA Cross Correlation FFT Fuzzy HHT PSDA ‎SSVEP‎

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