The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).
CLASSIFICATION OF MENTAL TASK FOR BRAIN COMPUTER INTERFACE USING ARTIFICIAL NEURAL NETWORK