P300 ve DHGUP Tabanlı Hibrid BBA Heceleyicisi için Yeni bir Yaklaşım

Abstract

Hem P300 hem de durağan hal görsel uyarılmış potansiyel (DHGUP) yüksek sinyal yanıtı ve yüksek sinyal gürültü oranına sahip olduğu için beyin bilgisayar arayüzü (BBA) sistemlerinde yaygın olarak kullanılan elektroensefalografi (EEG) fenomenleridir. Sinyallerdeki sınıflandırma doğruluk oranı ve sinyal tespit süresi değerleri BBA sistemlerinin performansını etkiler. Bu iki değer bir BBA sistemi için anahtar performans göstergesi olan bilgi aktarım hızının (BAH) hesaplanması için kullanılır. Bir P300 tabanlı BBA veya DHGUP tabanlı BBA diğer BBA sistemlerine göre daha yüksek bilgi aktarım hızı değerine sahiptir. Bundan dolayı, bu çalışmadaki amacımız, P300 ve DHGUP fenomenini aynı anda bir BBA heceleyicisinde kullanmaktır. P300 ve DHGUP tabanlı yenir bir hibrid BBA heceleyicisini çalışmamız ile sunuyoruz. Ayrıca önerdiğimiz BBA heceleyecisi sadece P300 uyaranlı veya sadece DHGUP ya da hybrid uyaranlı çalışmayada olanak vermektedir. Bu BBA heceleyicisinde 3 × 3 matris formunda 9 sayı P300 sinyali elde etmek için vardır ve üstelik 9 adet beyaz kare şeklinde yanıp sönen objeler DHGUP elde etmek için sayıların yanına yerleştirilmiştir. Bu araştırmada, deneyler 2 adımda (eğitim ve çevirimçi adımlar) ve 3 oturumda (sadece DHGUP uyaran, sadece P300 uyaran ve hybrid uyaran oturumlar) gerçekleştirilmiştir. Beş farklı kullanıcı deneylere katılmıştır. P300 sinyali ve DHGUP tespiti için destek vektör makinesi metodu kullanılmıştır. Deney sonuçlarına göre ortalama sınıflandırma doğruluk oranı sırasıyla sadece DHGUP uyaran kullanarak %83.78, sadece P300 uyaran kullanarak %84.67 ve hybrid uyaran kullanarak %90.89’ dur. Ortalama bilgi aktarım hızı sırasıyla DHGUP uyaran kullanarak 6.81, sadece P300 uyaran kullanarak 6.97 bit/dk ve hybrid uyaran kullanarak 8.19’ dur. Elde edilen bulgulara göre, önerilen P300 ve DHGUP tabanlı BBA heceleyicisi, sadece DHGUP uyaranlı BBA ya da sadece P300 uyaranı BBA heceleyicilerine göre daha yüksek sınıflandırma doğruluk oranına ve bilgi aktarım hızına ulaşmıştır.

Keywords

• EEG,Beyin Bilgisayar Arayüzü,Destek Vektör Makinesi,P300,DHGUP

A new Approach for Hybrid BCI speller based on P300 and SSVEP

Abstract

P300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in brain computer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate of signal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation information transfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITR values than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCI speller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use only P300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signal and also 9 white square flickering objects were placed near numbers for eliciting SSVEP. In this research, experiments were performed in two stage (training and online stages) with three sessions (only SSVEP stimuli session, only P300 stimuli session, and hybrid session). Five subjects participated experiments. We used support vector machine method for detection of P300 signal and SSVEP. According to experiment results, average classification accuracy values were 83.78%, 84.67%, and 90.89% with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Furhermore, average information transfer rate values were 6.81, 6.97, and, 8.19 bit/min with using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Results showed that the proposed hybrid BCI speller based on P300 and SSVEP reached higher classification accuracy and ITR values than using only SSVEP stimuli or only P300 stimuli based BCI spellers.

Keywords

• EEG,Brain Computer Interface,Support Vector Machine,P300,SSVEP

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