A high performance hybrid SSVEP based BCI speller system

The existing EEG based keyboard/speller systems have a tradeoff between the target detection time and classification accuracy. This study focuses on increasing the accuracy and probability of target classification rates in the SSVEP based speller system. We proposed two different types of hybrid SSVEP system by combining SSVEP with vision based eye gaze tracker (VET) and electro-oculogram (EOG). Thirty six targets were randomly chosen for this study and their corresponding visual stimulus was presented with unique frequencies. The visual stimuli were segregated into three groups and each group were arranged into different regions (left/middle/right) of the keyboard/speller layout for improving the probability of target detection rate. The VET/EOG data were utilized to identify the regions that belong to the selected target. The region/group determination decreases the issue of misclassification of SSVEP frequencies. The averaged spelling accuracies of SSVEP-VET and SSVEP-EOG system for all the subjects is 91.2% and 91.39% respectively. Later, a visual feedback was added to the SSVEP-EOG system (SSVEP-EOG-VF) for improving the target detection rate. In this case, an average classification accuracy of 98.33% was obtained with the information transfer rate (ITR) of 69.21 bits/min for all the subjects. An accuracy of 100% was obtained for five subjects with the ITR of 74.1 bits/min in this system.