  Speeding up the spelling in event-related potentials (ERP) based
Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a
short span of time, as much as the accurate classification of such evoked
potentials remains challenging and imposes hard constraints for signal
processing and machine learning techniques. Recent advances in stimulus
presentation and deep learning showcased a promising direction in significantly
improving the efficacy of those systems, in this study we propose the
combination of colored inverted face stimulation with classification using
convolutional neural networks in the hard settings of dry electrodes and fast
flashing single-trial ERP-based BCI. The high online accuracy achieved, with
two subjects passing the 90 percent correct symbol detection bar and a transfer
rate above 60 bits per minute, demonstrates the approach potential in improving
the practicality of ERP based BCIs.
