title: Incorporating advanced language models into the P300 speller using particle filtering.
abstract: OBJECTIVE
  The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram 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.
  
  
  METHODS
  Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model.
  
  
  RESULTS
  This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method.
  
  
  CONCLUSIONS
  These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA 90095, USA.
authors.forename: W
authors.initials: W
authors.lastname: Speier
authors.forename: C W
authors.initials: CW
authors.lastname: Arnold
authors.forename: A
authors.initials: A
authors.lastname: Deshpande
authors.forename: J
authors.initials: J
authors.lastname: Knall
authors.forename: N
authors.initials: N
authors.lastname: Pouratian
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013647:Task Performance and Analysis; D015443:Word Processing / Q000379:methods