@thesis{5132,
  title = {Error Correcting Codes for the P300 Visual Speller},
  abstract = {The aim of brain-computer interface (BCI) research is to establish
  a communication system based on intentional modulation of brain
  activity. This is accomplished by classifying patterns of brain ac-
  tivity, volitionally induced by the user. The BCI presented in this
  study is based on a classical paradigm as proposed by (Farwell and
  Donchin, 1988), the P300 visual speller. Recording electroencephalo-
  grams (EEG) from the scalp while presenting letters successively to
  the user, the speller can infer from the brain signal which letter the
  user was focussing on. Since EEG recordings are noisy, usually many
  repetitions are needed to detect the correct letter. The focus of this
  study was to improve the accuracy of the visual speller applying some
  basic principles from information theory: Stimulus sequences of the
  speller have been modified into error-correcting codes. Additionally
  a language model was incorporated into the probabilistic letter de-
  coder. Classification of single EEG epochs was less accurate using
  error correcting codes. However, the novel code could compensate for
  that such that overall, letter accuracies were as high as or even higher
  than for classical stimulus codes. In particular at high noise levels,
  error-correcting decoding achieved higher letter accuracies.},
  degree_type = {Diplom},
  institution = {Eberhard-Karls-Universität Tübingen, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2007},
  author = {Biessmann, F.},
  month_numeric = {7}
}