Brain Computer Interface (BCI) using Steady State Visual Evoked Potentials (SSVEPs)

This thesis work presents the major challenges and solutions for the Brain Computer Interfaces (BCIs), which are based on Steady State Visual Evoked Potentials (SSVEPs). A BCI utilizes the information transmitted by the brain; there are several methods available for analyzing these brain activities. One of the major challenges in BCI is to remove the noise successfully. Different methods are investigated in this thesis work for de-noising the brain activities. In this thesis work, three different approaches have been investigated to estimate the frequency spectrum of the brain signals. In addition to the well known Fourier Transform (FT) technique, for spectral estimation, there are many parametric and non parametric techniques for computing the frequency content of a signal. This thesis work presents a comparison between Fast Fourier Transform (FFT), MUltiple SIgnal Classification (MUSIC) and Linear Predictive Coding (LPC). The effectiveness of different techniques has been studied and the simulation results have shown that MUSIC outperforms the other approaches. The SSVEP data is provided by g.tec Guger Technologies for simulations in this thesis work. MATLAB has been utilized to conduct the simulations.