Examining the Effect of Dimension Reduction on EEG Signals by K-Nearest Neighbors Algorithm

Abstract

Machine learning which a paradigm of methods that makes inferences from existing data using mathematical and statistical methods and is inferred to be unknown. The proposed method in this paper, supervised learning algorithm is applied to EEG (electroencephalography) data and classification algorithm performance is analyzed and results are examined in MATLAB. K-Nearest Neighbors Algorithm (k-NN) is used in this paper as algorithm. This classification was evaluated in two stages, with and without Principal Component Analysis (PCA). Dimension reduction is the process of reducing the size of dimension of the data. By reducing the size of the data set with PCA, it is expected to protect important data features. KNN has given results that can be regarded as prudent in terms of classification accuracy. The results of the present work showed that appropriate features combined with classifier can be done significant classification for different bioelectrical signal

Keywords

• Denetimli öğrenme algoritması; En yakın komşu algoritması (kNN); Temel Bileşen Analizi; Boyut Azaltma; EEG

En Yakın Komşu Algoritması Kullanılarak EEG Sinyallerine Boyut Azaltmanın Etkilerinin İncelenmesi

Abstract

Makine öğrenmesi, var olan verilerin çıkarımlarını matematiksel ve istatistiksel yöntemlerle yapan ve bilinmeyen bir yöntem paradigmasıdır. Bu çalışmada, denetimli öğrenme algoritması, EEG (elektroensefalografi) verilerine uygulanmış, sınıflandırma algoritması performans analiz sonuçları MATLAB ile incelenmiştir. Bu çalışmada, algoritma olarak en yakın komşu algoritması (k-NN) kullanılmıştır. Bu sınıflandırma, Temel Bileşen Analizinin (TBA) kullanıldığı ve kullanılmadığı durumlar için iki aşamada değerlendirilmiştir. Boyut azaltma, verilerin boyut boyutunu küçültme işlemidir. TBA ile veri kümesinin boyutunun azaltılarak, önemli veri özelliklerini korunması beklenir. KNN, sınıflandırma doğruluğu açısından önemli sayılabilecek sonuçlar vermiştir. Mevcut çalışma, farklı biyoelektriksel sinyaller için uygun özelliklerin uygun bir sınıflandırıcı ile kombine edildiğinde anlamlı bir sınıflandırma yapılabileceğini göstermiştir.

Keywords

• Supervised learning algorithms; k-nearest neighbors algorithm (kNN); Principal Component Analysis (PCA); Dimension reduction; EEG

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