Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.

Published

Journal Article

P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies ( > 70%) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behavior or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform offline simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that flagged, deleted and replaced erroneous characters (-47-0%) . Providing additional information about the likelihood of characters to a dictionary-based correction further improves accuracy. Our Bayesian dictionary-based correction algorithm that utilizes P300 classifier confidences performed comparably (44-416%) to an oracle ErrP dictionary-based method that assumed perfect ErrP classification (43-433%).

Full Text

Duke Authors

Cited Authors

  • Mainsah, BO; Morton, KD; Collins, LM; Sellers, EW; Throckmorton, CS

Published Date

  • September 2015

Published In

Volume / Issue

  • 23 / 5

Start / End Page

  • 737 - 743

PubMed ID

  • 25438320

Pubmed Central ID

  • 25438320

Electronic International Standard Serial Number (EISSN)

  • 1558-0210

International Standard Serial Number (ISSN)

  • 1534-4320

Digital Object Identifier (DOI)

  • 10.1109/TNSRE.2014.2374471

Language

  • eng