Integrating language models into classifiers for BCI communication: A review

W. Speier, C. Arnold, N. Pouratian

Research output: Contribution to journalReview articlepeer-review

Abstract

Objective. The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. Approach. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Main results. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Significance. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

Original languageEnglish (US)
Article number031002
JournalJournal of neural engineering
Volume13
Issue number3
DOIs
StatePublished - May 6 2016
Externally publishedYes

Keywords

  • braincomputer interface
  • language model
  • natural language processing
  • P300 speller
  • predictive spelling

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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