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Abstract

The Visual Speller is a Brain-computer interface (BCI) based on ERP's. In the classical paradigm one trial consists of successive highlightings of one or more symbol(s) on a visual grid presented to the subject. The stimulus events in which the symbol of interest was highlighted will result in an enhanced ERP. The more symbols are highlighted simultaneously the faster the speller gets. A stimulus code that uses few events per trial is called dense. The tradeoff with dense codes is that the signal to noise ratio gets worse with increasing stimulus frequency: the P300 signal is reported to be strongest when the target symbol frequency is lowest. The stimulus code in which only one symbol per stimulus event is presented, is a maximally sparse code. It has been proposed that high bitrates of information transfer in a visual speller can best be achieved with sparse stimulus codes. However sparse codes have long trial durations. In order to improve the information transfer rate, we tried to use denser stimulus codes that present fewer stimulus events per trial. To investigate the effect of stimulus type on classification accuracy and the interdependence of stimulus code and type, we explored new stimulus types including ones exploiting recent findings in neuropsychology, such as change blindness and isoluminant color motion. We show that, using appropriate stimuli, denser codes, and hence fewer stimulus events, yield sufficient classification accuracy to achieve competitive bitrates.