Disjunctive normal unsupervised LDA for P300-based brain-computer interfaces
Elwardy, Majed Mahmoud Ramadan and Taşdizen, Tolga and Çetin, Müjdat (2016) Disjunctive normal unsupervised LDA for P300-based brain-computer interfaces. In: 24th Signal Processing and Communication Application Conference (SIU 2016), Zonguldak, Turkey
Official URL: http://dx.doi.org/10.1109/SIU.2016.7496226
Can people use text-entry based brain-computer interface (BCI) systems and start a free spelling mode without any calibration session? Brain activities differ largely across people and across sessions for the same user. Thus, how can the text-entry system classify the desired character among the other characters in the P300-based BCI speller matrix? In this paper, we introduce a new unsupervised classifier for a P300-based BCI speller, which uses a disjunctive normal form representation to define an energy function involving a logistic sigmoid function for classification. Our proposed classifier updates the initialized random weights performing classification for the P300 signals from the recorded data exploiting the knowledge of the sequence of row/column highlights. To verify the effectiveness of the proposed method, we performed an experimental analysis on data from 7 healthy subjects, collected in our laboratory. We compare the proposed unsupervised method to a baseline supervised linear discriminant analysis (LDA) classifier and demonstrate its effectiveness.
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