A Study on the Effect of Features Obtained From Signal Segments on Classification Success

Year 2021, Issue: 32, 383 - 391, 31.12.2021

Erdem Erkan , Yasemin Erkan

https://doi.org/10.31590/ejosat.1040429

Abstract

Successful classification depends on the selection of the distinctive features and the effective channel subset used in the classification. In this study, novel and practical methods are proposed for determining the distinctive features and detecting effective channel subsets in the multi channel classification systems such as EEG. Two different feature extraction methods are compared in the study. The first one is based on classical Wavelet transform and the second is our proposed approach which used the slope of signal segments. Feature vectors are generated from some signal properties such as the mean, standard deviation, numerical integral of the Wavelet coefficients for classical Wavelet transform based feature extraction method. For our proposed method, only the slopes of signal segments are used for the feature vectors. In the proposed Signal Path Slope (SPS) feature extraction method, differently from the classical Wavelet based method, a Savitzky Golay (S-G) filter with an optimal frame length is applied to the signal before segmentation to make the path of the signal more prominent in time domain. In this way, the distinctive classification features are extracted by using S-G filter. For channel selection, an iterative channel selection method based on the classification results which divide the dataset labelled dataset into two groups as % 90 pre-training and %10 pre-test data is proposed. The dataset provided as dataset-3 in BCI competition IV is used in this study. The feature vectors extracted by using the proposed methods are classified for each method with the Support Vector Machine classifier. The results are given comparatively and it is observed that our proposed method has less computational complexity and more successful classification than Wavelet based classical feature extraction methods. The highest classification accuracies of % 67.74 and % 49.27 for subject-1 and subject-2 respectively are obtained with a low dimensional feature vector by proposed SPS feature extraction method. The classification accuracies achieved in the study are increased by % 8.24 for subject-1 and % 14.97 for subject-2 when compared average of the competition results. The significant increase in the success for both subjects shows the consistency of the proposed methods. By this study, it is observed that there is a subject-specific signal pattern related to motor imagery tasks in the brain. This pattern distinctive features is successfully determined by using the proposed methods.

Keywords

Brain Computer Interface, Classification, Feature Vector, Channel Selection, Savitzky Golay

Sinyal Segmentlerinden Elde Edilen Özniteliklerin Sınıflandırma Başarısına Etkisi Üzerine Bir Araştırma

Abstract

Başarılı sınıflandırma, ayırt edici özniteliklerin ve sınıflandırmada kullanılan etkin kanal alt kümesinin seçimine bağlıdır. Bu çalışmada, EEG gibi çok kanallı sınıflandırma sistemlerinde ayırıcı özniteliklerin belirlenmesi ve etkin kanal alt kümelerinin saptanması için yeni ve pratik yöntemler önerilmiş ve iki farklı öznitelik çıkarma yöntemi karşılaştırılmıştır. Bunlardan ilki, klasik Dalgacık dönüşümüne ve
ikincisi de sinyal segmentlerinin eğimini kullanan önerilen yaklaşımımızdır. Klasik Dalgacık dönüşümü tabanlı öznitelik çıkarma
yöntemi için Dalgacık katsayılarının ortalama, standart sapma, sayısal integrali gibi bazı sinyal özelliklerinden öznitelik vektörleri üretilir. Önerilen, Sinyal Yolu Eğimi (SPS) yöntemi için ise öznitelik vektörleri sadece sinyal segmentlerinin eğimlerinden oluşmaktadır. Önerdiğimiz öznitelik çıkarma yönteminde, klasik Dalgacık tabanlı yöntemden farklı olarak, segmentasyondan önce sinyale zaman domeninde optimal çerçeve uzunluğuna sahip bir Savitzky Golay (SG) filtresi uygulanarak sinyal yolunun daha belirgin hale getirilmesi sağlanmıştır. Bu sayede SG filtresi kullanılarak ayırt edici sınıflandırma öznitelikleri çıkarılmaktadır. Kanal seçimi için, eğitim veri kümesi %90 ön eğitim ve %10 ön test verisi olarak iki gruba ayıran iteratif bir kanal seçim yöntemi önerilmiştir. Çalışmada BCI yarışması IV'te sunulan veri seti-3 kullanılmıştır. Önerilen yöntemler kullanılarak çıkarılan öznitelik vektörleri Destek Vektör makinesi sınıflandırıcısına tabi tutulmuştur. Sonuçlar karşılaştırmalı olarak verilmiş ve önerilen yöntemimizin Wavelet tabanlı klasik öznitelik çıkarma yöntemlerine göre daha az hesaplama karmaşıklığına ve daha başarılı sınıflandırma kabiliyetine sahip olduğu gözlemlenmiştir. Denek-1 ve denek-2 için sırasıyla % 67.74 ve % 49.27 olan en yüksek sınıflandırma doğruluğu, önerilen SPS öznitelik çıkarma yöntemi ile düşük boyutlu bir öznitelik vektörü ile elde edilmiştir. Çalışmada elde edilen sınıflandırma başarımı, yarışma elde edilen sonuçlarla karşılaştırıldığında, denek-1 için % 8.24 ve denek-2 için % 14.97 oranında sınıflandırma başarısı artışı gözlemlenmiştir. Her iki denek için de başarıdaki önemli artış, önerilen yöntemlerin tutarlılığını göstermektedir. Bu çalışma ile beyinde motor imgeleme görevleriyle ilgili deneğe özgü bir sinyal örüntüsü olduğu gözlemlenmiştir. Bu örüntünün ayırt edici özellikleri önerilen yöntemler kullanılarak başarılı bir şekilde tespit edilmiştir.

Keywords

Beyin Bilgisayar Arayüzü, Sınıflandırma, Öznitelik Vektörü, Kanal Seçimi, Savitzky Golay

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