Etkin epoklar ile motor hayaline dayalı EEG işaretlerinin sınıflandırma doğruluğunun artırılması

Yıl 2018, Cilt: 24 Sayı: 5, 817 - 823, 12.10.2018

Ebru Ergün Önder Aydemir

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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