User Independency of SSVEP Based Brain Computer Interface Using ANN Classifier: Statistical Approach

Abstract

BCIs, which elaborated as Brain-computer Interface that use brain responses to control the BCI paradigms. These brain responses are measured using Electroencephalographic signal along the scalp of the subjects. However, the less variability of EEG signal from the subjects make the BCI paradigms user independent. In this research, we simply analyze the user independency of SSVEP based EEG signal that makes a conclusion inter subject’s variability of BCI users. To accomplish the research goal, SSVEP based EEG signal extract from both different subjects and different stimulation conditions and a features vector is formed to compare each subject’s variability. Artificial Neural Network classifier is used to determine the deviation and regression of deviation of each features vectors. From the heatmap and classifier, it is found that the used independency of the EEG signal is less that means that less variability of EEG. That ensures the user independent BCI paradigms with high transfer rate of the bits.