Integrating language information with a hidden markov model to improve communication rate in the P300 speller

William Speier, Corey Arnold, Jessica Lu, Aniket Deshpande, Nader Pouratian

Research output: Contribution to journalArticlepeer-review

Abstract

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 (EEG) signal. Information about the structure of natural language can be valuable for BCI communication systems, but few attempts have been made to incorporate this domain knowledge into the classifier. In this study, we treat BCI communication as a hidden Markov model (HMM) where hidden states are target characters and the EEG signal is the visible output. Using the Viterbi algorithm, language information can be incorporated in classification and errors can be corrected automatically. This method was first evaluated offline on a dataset of 15 healthy subjects who had a significant increase in bit rate from a previously published naïve Bayes approach and an average 32% increase from standard classification with dynamic stopping. An online pilot study of five healthy subjects verified these results as the average bit rate achieved using the HMM method was significantly higher than that using the naïve Bayes and standard methods. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance and accuracy.

Original languageEnglish (US)
Article number6718098
Pages (from-to)678-684
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number3
DOIs
StatePublished - May 2014
Externally publishedYes

Keywords

  • Brain-computer interfaces (BCIs)
  • electro encephalography (EEG)
  • hidden Markov models (HMMs)
  • natural language processing

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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