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Electroencephalography based brain computer interface systems provide communication with the environmental devices for users who cannot use neuromuscular system. There are various brain signal activities for EEG-based BCIs. P300 is a type of event related potential and is a convenient signal type for BCI systems. One of the most important performance parameters is the classification accuracy rate for a P300-based BCI. In this study we aimed to obtain a higher accuracy rate with the changes of row column based P300 speller structure. Changes were made to the matrix structure of P300 speller and inter stimulus interval duration. our new approach region based P300 speller. In most of previous studies are about changes of stimlus color and shapes in row column based P300 speller. In this research experiments with 4 different modes P300 speller were used for character selection. The EEG recordings that collected in the experiments were pre-processed and then classified by stepwise linear discriminant analysis. Acording to the classification result the highest average of the classification accuracy was reached to 84.76% in the experiments with 4x4 matrix based P300 speller with 150 ms inter stimulus interval duration. In the contrary of this the lowest classification accuracy was observed with 6x6 matrix based P300 speller with 300 ms inter stimulus interval duration. This study showed that the row column based P300 speller can be achieved to high accuracy rate with changes in stimulus matrix structure and inter stimulus interval duration.

P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde Uyaranlar Arası Sürenin ve Uyaran Yapısının Performansa Etkisi