The Effect that Auxiliary Taxonomized Auditory Distractions have on a P300 Speller while utilising Low Fidelity Equipment In this paper, we ensue on the development of a taxonomy aimed at categorizing distractions in the P300b domain. Explicitly, we investigate the effect that auditory distractions, distinctively that of ambient noise (AN), passive talking (PT), and active listening (AL) have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. This work is part of a larger EEG based project and is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with eight healthy subjects; while using a noninvasive BrainComputer Interface based on low fidelity electroencephalographic (EEG) equipment. Our results show that the accuracy was best for the Lab (LC) at 100%, followed by AN at 92.5%, PT at 90% and last AL at 87.5%, which results were in identical order to the subjectsxe2x80x99 preferences. In addition, the amplitude and latency did not show any statistical significance in all settings. This paper provides additional results that impart insight into the practicability of the aforementioned P300 speller methodology and lowcost equipment to be used in realworld applications.

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.