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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 modiamp;amp;amp;64257;ed into error-correcting codes. Additionally
a language model was incorporated into the probabilistic letter de-
coder. Classiamp;amp;amp;64257;cation 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.