Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application

Abstract

Brain-computer interfaces (BCIs) recently have been focusing on combining various BCI modalities to form different combinations of hybrid BCIs. These paradigms are designed to elicit more than one brain potential in the form of BCI features. This research is being carried out with the objective of increasing classification accuracy and information transfer rate (ITR) based on measurement of brain potentials. This study proposed a novel hybrid BCI elicitation and measurement technique combining steady-state visually evoked potential (SSVEP) and P300 potentials to increase the ITR. The hybrid BCI also increased the number of target options compared to SSVEP paradigm for a set number of presumed frequencies of flickering. One of the hybrid BCIs used distinct colours along with distinct flickering frequencies for targets, with an aim to increase the accuracy of classification and reduction of system uncertainty parameter known as false activation rate (FAR). The results of a study in 10 volunteers established that the novel SSVEP-P300 hybrid BCI with distinct colours for target frequencies had average parameters as follows: classification accuracy of 90.76%, ITR of 81.10 bits/min and FAR of 2.99%. A comparative study of the two novel paradigms with SSVEP and P300 paradigms in the same environment was conducted. The results of the comparative study concluded that the hybrid BCI with distinct colours for various target frequencies yielded the best results and hence can be considered as a viable paradigm option for the development of an assistive device.