Etkin epoklar ile motor hayaline dayalı EEG işaretlerinin sınıflandırma doğruluğunun artırılması

Öz

Beyin bilgisayar arayüzleri (BBA) sadece beyinde üretilen işaretleri kullanarak çeşitli elektronik cihazları kullanmayı olanaklı hale getiren sistemlerdir. Bu sistemlerin yüksek başarımlı olabilmesi için bu işaretlerden çıkarılan öznitelik yöntemleri ve bu işaretlere uygulanan sınıflandırıcı yöntemleri önemlidir. Bu çalışma ile motor hayaline dair kaydedilen EEG tabanlı BBA işaretlerinden yüksek sınıflandırma doğruluğu elde edebilmek için işaretlerin etkin zaman dilimlerinden çıkarılmış özniteliklerle sınıflandırma doğruluğunun artırılmasına yönelik bir yöntem önerilmiştir. Öznitelikler, etkin zaman dilimleri belirlenen EEG işaretlerine Hilbert Dönüşümü’nün uygulanması ve işaretin türevlerinin ortalamasının alınmasıyla elde edilmiştir. BCI Competition 2003 yarışmasında kullanıma sunulmuş 2-sınıflı motor hareketi hayaline dayalı Data Set Ia isimli veri kümesinden çıkarılan öznitelikler destek vektör makineleri, k-en yakın komşuluk ve doğrusal ayrım analizi ile test edilerek performans karşılaştırması yapılmıştır. Destek vektör makineleri ile test veri kümesi üzerinde %91.46 oranında yüksek bir sınıflandırma doğruluğu elde edilmiştir. Bu sınıflandırma doğruluğu EEG işaretinin bir denemesine ait tüm örneklemelerin kullanılması durumunda elde edilen sınıflandırma doğruluğundan %17.40 daha yüksektir. Elde edilen sonuçlar, önerilen yöntemin belirlenen öznitelik çıkarma yöntemi ve destek vektör makinaları sınıflandırıcısıyla birlikte EEG işaretlerinden elde edilen sınıflandırma doğruluğunu dikkat çekici miktarda arttırdığını ve hesaplama karmaşıklığını ise azalttığını göstermiştir.

Anahtar Kelimeler

• Elektroensefalografi,Beyin bilgisayar arayüzü,Hilbert dönüşümü,Etkin zaman dilimi,Etkin epok,Destek vektör makinası

Improving classification accuracy of motor imagery EEG signals via effective epochs

Abstract

Brain computer interfaces (BCI) are systems which make it possible to use various electronic devices using only the signals produced in the brain.  In order to ensure high performance of these systems, feature methods extracted from these signals and classifier methods applied to these signals are important. With this study, we proposed a method to obtain high classification accuracy from EEG based BBA signals recorded on the motor imaginary with the extracted features in the active time segments. Features were obtained by applying the Hilbert Transform to the active time segments selected EEG signs and calculating the average of the derivatives of the signs. Features extracted from two-class motor imaginary Data Set Ia (Presented at the BCI Competition 2003 competition) were analyzed by support vector machines, k-nearest neighborhood and linear discriminant analysis. Then the performance of the classifiers was compared. A high classification accuracy of 91.12% is calculated on the test dataset with support vector machines. This classification accuracy is 17.06% higher than the classification accuracy obtained in the case of using all samples of a trial of the EEG signal. As a result, the proposed method increased the accuracy of classification in a remarkable amount and reduced computational complexity with the feature extraction methods and support vector machine classifier.

Keywords

• Electroencephalography,Brain computer interface,Hilbert transformation,Active time segment,Effective epoch,Support vector machine

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