Study of power spectrum estimation of steady-state visual evoked potential-based BCI system using AR model approach

Abstract

Brain-computer interface (BCI) system is the direct interaction between the human brain and the external electronic devices like robotic arms, electronic wheel chair, etc., through desired mental tasks which enable the different amplitude of brainwaves inferring to humans’ different mental activities. It uses the electrical activity of brain caused by the communication between the two neurons. The neurons communicate with each other in the form of electrical impulses which generate low amplitude and low frequency electromagnetic wave termed as electroencephalogram (EEG), In the past years, different Paradigms have been used to design BCI like Motor imaginary, P300, SSVEP, etc. The objective of this paper is to estimate the power spectral density (PSD) of the SSVEP signal (Steady-state Visual evoke Potential) from recoded EEG Data using AR Model approach.