Incorporating advanced language models into the P300 speller using particle filtering

摘要

ObjectiveThe P300 speller is a common brain–computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram (EEG) signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity.