Performance Analysis of SSVEP Based Wireless Brain Computer Interface for Wet and Dry Electrode

Abstract

A Brain-computer Interface (BCI) is a communication pathway to provide ease to the users for interacting with the outside surroundings after translating brain signals into machine commands. The modern Steady-state Visual Evoked Potential (SSVEP) based Electroencephalographic (EEG) signals has become the most sophisticated methodology for a BCI paradigms. So, the perfection of SSVEP signal make the perfection of the BCI paradigm. The use of gel based wet electrode for the extraction of EEG signal is too much noisy and unpredictable for long time measurement which degrades the quality of SSVEP signal in a consequence degrades the performance of modern BCI paradigm. In our research, we are trying to solve this degradation of the quality of SSVEP signal. To accomplish this goal, a typical wireless BCIs using dry electrode is proposed for long term application without sacrificing Information Transfer Rate (ITR), Signal to Noise Ratio (SNR). After extracting SSVEP signal using dry electrode, Analog to Digital Conversion (ADC) is proceeded for the wireless transmission for remote BCI paradigms. Finally, after receiving this signal any BCI paradigms can be operated with high degree of accuracy.