Exploiting P300 amplitude variations can improve classification accuracy in Donchin's BCI speller

The P300 is an endogenous component of EEG event related potentials which is elicited by rare and significant stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because, being naturally elicited in response to external stimuli, users do not need special training. However, P300 waves are hard to detect and, therefore, multiple stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves and its exploitation in BCI. In this paper we start filling this gap, by first documenting and then exploiting a modulation in amplitude of P300 caused by target-to-target interval (TTI) differences. We demonstrate this within the context of the Donchin's speller, which is perhaps the best known example of a BCI system relying on the detection P300 waves, where target-to-target interval variations are induced by stimuli randomisation. In particular we show that by specialising detectors to work with P300s elicited with each TTI, we can further improve the performance of the best known Donchin's speller with minimal changes.