Error Correcting Codes for the P300 Visual Speller

2007

Thesis

ei

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.

@thesis{5132,
  title = {Error Correcting Codes for the P300 Visual Speller},
  author = {Biessmann, F.},
  institution = {Eberhard-Karls-Universität Tübingen, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2007},
  doi = {},
  month_numeric = {7}
}