Online BCI typing using language model classifiers by ALS patients in their homes

William Speier, Nand Chandravadia, Dustin Roberts, Shrita Pendekanti, Nader Pouratian

Research output: Contribution to journalArticlepeer-review

Abstract

The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but this has largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from 2 to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.

Original languageEnglish (US)
Pages (from-to)114-121
Number of pages8
JournalBrain-Computer Interfaces
Volume4
Issue number1-2
DOIs
StatePublished - Apr 3 2017
Externally publishedYes

Keywords

  • amyotrophic lateral sclerosis
  • augmentative and alternative communication
  • electroencephalography
  • P300 speller

ASJC Scopus subject areas

  • Biomedical Engineering
  • Human-Computer Interaction
  • Behavioral Neuroscience
  • Electrical and Electronic Engineering

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