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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Comparison of Dimension Reduction Algorithms on EEG Signals

EEG İşaretlerinde Boyut İndirgeme Algoritmalarının Karşılaştırılması

How to cite: Özsandıkçıoğlu , Atasoy A, Kablan Y, Sevim Y, Aykut M. Comparison of dimension reduction algorithms on eeg signals. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(2): 140-144. DOI: 10.54856/jiswa.201812043

Full Text: PDF, in Turkish.

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Title: Comparison of Dimension Reduction Algorithms on EEG Signals

Abstract: Like in all classification applications, the most important process which increases classification success of electroencephalography (EEG) applications is to choose the proper features for signals. Since there is not certain feature extraction method for data classification applications, used feature matrix size can be redundantly large and this state effect the system's speed and success negatively. In this study Data Set III of BCI competition 2003 was used. We extract features using this data set and then dimension of feature matrix size reduced by using Principal Component Analysis, Kernel Principal Component Analysis and Locality Preserving Projections method which is alternative to Principal Component Analysis. As a result, the best success rate is obtained as 83.28% when Linearity Preserving Projections algorithm with Chebycev distance measuring method is used.

Keywords: EEG; principal component analysis; kernel principal component analysis; lcality preserving projection


Başlık: EEG İşaretlerinde Boyut İndirgeme Algoritmalarının Karşılaştırılması

Özet: Bütün sınıflandırma algoritmalarında olduğu gibi, elektroensefalografi (EEG) uygulamalarının sınıflandırma başarısını arttıran en önemli işlem, işarete ait uygun özniteliklerin elde edilmesi aşamasıdır. Sınıflandırma uygulamalarında belirli bir öznitelik çıkarma metodu bulunmadığından dolayı, seçilen özniteliklerin oluşturduğu öznitelik matrisi büyük boyutlarda olabilmekte ve bu durum sistemin hızını ve başarısını olumsuz olarak etkilemektedir. Bu çalışmada, 'BCI Competition III' yarışmasında kullanılan III. veri seti kullanılmıştır. Bu veri seti için, Temel Bileşen Analizi, Çekirdek Temel Bileşen Analizi ve veri setindeki komşuluk yapısını koruyarak verinin projeksiyonunu sağlayan, Temel Bileşen Analizine alternatif yöntem olan Yerellik Koruyucu İzdüşüm algoritmaları kullanılarak öznitelik matrisi üzerinde boyut azaltma işlemi yapılmıştır. Sonuç olarak, Chebyshev mesafe ölçümü yöntemini kullanan Yerellik Koruyucu İzdüşüm algoritması ile en yüksek başarı % 83.28 olarak elde edilmiştir.

Anahtar kelimeler: EEG; Temel Bileşen Analizi; Çekirdek Temel Bileşen Analizi; Yerellik Koruyucu İzdüşüm algoritmasıEEG; temel bileşen analizi; çekirdek temel bileşen analizi; yerellik koruyucu izdüşüm algoritması


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