Study on Novel Designs with Reduced Fatigue for Steady State Motion Visual Evoked Potentials The paper focuses on incorporation of Brain Computer Interfacing (BCI) within an Augmented Reality (AR) platform to provide means for individuals with communication disabilities to interact with the outer world. Recently, there has been a recent surge of interest on SteadyState Visual Evoked Potentials (SSVEP). In a typical SSVEPbased BCI system, the virtual object within the AR environment flickers with a specific frequency while the signal processing module extracts the effects of the flickering frequency on the Electrophysiological (EEG) signals. Despite the popularity of SSVEPs, their utilization for practical application especially for assistive technologies is complicated and challenging due to eye fatigue and risk of induced epileptic seizure. In this regard, the key issue being targeted in this paper is addressing fatigue of flicker (or brightness modulation) by development of flickerfree steadystate motion visual evoked potential (SSMVEP). Two novel SSMVEP paradigms, i.e., Squarebased and Circlebased paradigms, with low luminance contrast and oscillating expansion and contraction motions are designed, and integrated within a BCI system. Through experimental evaluations, high detection accuracy of 95.31% is achieved for the squarebased SSMVEP.

A Hybrid Speller Design Using Eye Tracking and SSVEP Brainxe2x80x93Computer Interface Steadystate visual evoked potentials (SSVEPs) have been extensively utilized to develop brainxe2x80x93computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuliresponsive hybrid speller by using electroencephalography (EEG) and videobased eyetracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCIspeller uses only six frequencies to classify fortyeight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCIspellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 xc2xb1 3.597% with an average ITR of 184.06 xc2xb1 12.761 bits per minute in a cuedspelling task and an ITR of 190.73 xc2xb1 17.849 bits per minute in a freespelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCIbased system will ultimately enable a truly highspeed communication channel.