Abstract
AbstractThe steady-state visual evoked potential (SSVEP) is a periodic signal contaminated with recorded electroencephalography (EEG) signal. Accurate detection of SSVEP signals from noise contaminated EEG signal is the key challenge to improve the performance of an SSVEP-based BCI system. Therefore, the use of a signal processing algorithm plays a significant role to detect the SSVEP signal with great accuracy. This paper describes the recent development in the use of various existing detection algorithms for the SSVEP BCI system. The signal processing technique related to preprocessing and feature extractions is discussed in this paper. This study report that technique that can be applied for non-stationary and nonlinear signals analysis are more employed as compared to traditional Fourier transform to improve the performance for SSVEP BCI system. Spatial filtering techniques are useful for channel selection and to eliminate the nuisance signal from multi-channel EEG signal.KeywordsSteady-state visual evoked potential (SSVEP)Canonical correlation analysis (CCA)Brain computer interface (BCI)Electroencephalography (EEG)Empirical mode decomposition (EMD)
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