• DocumentCode
    1485420
  • Title

    A Dictionary-Driven P300 Speller With a Modified Interface

  • Author

    Ahi, Sercan Taha ; Kambara, Hiroyuki ; Koike, Yasuharu

  • Author_Institution
    Dept. of Comput. Intell. & Syst. Sci., Tokyo Inst. of Technol., Yokohama, Japan
  • Volume
    19
  • Issue
    1
  • fYear
    2011
  • Firstpage
    6
  • Lastpage
    14
  • Abstract
    P300 spellers are mainly composed of an interface, by which alphanumerical characters are presented to users, and a classification system, which identifies the target character by using acquired EEG data. In this study, we proposed modifications both to the interface and to the classification system, in order to reduce the number of required stimulus repetitions and consequently boost the information transfer rate. We initially incorporated a custom-built dictionary into the classification system, and conducted a study on 14 healthy subjects who copy-spelled 15 four letter words. Incorporating the dictionary, the mean accuracy at five trials increased from 72.86% to 95.71%. To further increase the system performance, we first validated the hypothesis that for a conventional P300 system, most target-error pairs lie on the same row or column. Then based on the validated hypothesis, we adjusted letter positions on the well-known from A to Z interface. The same subjects spelled the same 15 words using the modified interface as well, and the mean information transfer rate at two trials reached 55.32 bits/min.
  • Keywords
    bioelectric potentials; brain-computer interfaces; electroencephalography; handicapped aids; spelling aids; EEG data; alphanumerical characters; classification system; dictionary driven P300 speller; information transfer rate; letter positions; modified interface; required stimulus repetitions; target character identification; Computational intelligence; Dictionaries; Electroencephalography; Genetic algorithms; Linear discriminant analysis; Materials science and technology; Permission; Support vector machine classification; Support vector machines; System performance; Brain–computer interface (BCI); P300 speller; electroencephalogram (EEG); genetic algorithms; spelling correction; Adult; Brain; Communication Aids for Disabled; Computer Peripherals; Electroencephalography; Event-Related Potentials, P300; Female; Humans; Male; Pattern Recognition, Automated; User-Computer Interface; Word Processing; Writing;
  • fLanguage
    English
  • Journal_Title
    Neural Systems and Rehabilitation Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1534-4320
  • Type

    jour

  • DOI
    10.1109/TNSRE.2010.2049373
  • Filename
    5460913