EEG Sinyalleri Kullanılarak Zihinsel İş Yükü Seviyelerinin Sınıflandırılması

Zihinsel iş yükü, bir görevi gerçekleştirmek için gerekli olan bilişsel kapasite miktarıdır. Elektroensefalogram (EEG), zihinsel iş yükünün objektif olarak değerlendirilebilmesi için kullanılan bir görüntüleme tekniğidir. Bu çalışmada, eşzamanlı görevlerin yerine getirilmesi sırasında kaydedilmiş EEG sinyallerinden zihinsel iş yükü seviyelerinin sınıflandırılması için, Katz fraktal boyut (KFB) ve Higuchi fraktal boyut (HFB) algoritmalarına dayalı öznitelik çıkarma yöntemleri ile hata düzelten çıkış kodlaması (HDÇK) yönteminin kullanılması önerilmiştir. Çok sınıflı sınıflandırma problemleri için önerilen bir sınıflandırıcı birleşim tekniği olan HDÇK, zihinsel iş yükünün düşük, orta ve yüksek seviye olarak sınıflandırılması için kullanılmıştır. HDÇK, destek vektör makineleri (DVM), k en yakın komşuluk ve kuadratik ayırtaç analizi yöntemleri kullanılarak bire-karşı-diğerleri yaklaşımı ile oluşturulmuştur. Önerilen yöntemin performansı, 48 katılımcıdan kaydedilen EEG sinyallerini içeren Eşzamanlı Görev EEG İş Yükü veri kümesi üzerinde değerlendirilmiştir. KFB ve HFB algoritmaları kullanılarak sınıflandırma doğrulukları sırasıyla %78.44 ve %95.39 ve Cohen’s Kappa değeri 0.52 ve 0.89 olarak belirlenmiştir. Sonuçlar, HFB ve DVM-HDÇK yöntemlerinin bir arada kullanımının zihinsel iş yükünün çok sınıflı sınıflandırılmasında başarılı bir yöntem olabileceğini göstermektedir.

Anahtar Kelimeler:

elektroensefalogram (EEG), zihinsel iş yükü, katz fraktal boyut, higuchi fraktal boyut, hata düzelten çıkış kodlaması (HDÇK)

Classification of Mental Workload Levels by Using EEG Signals

Mental workload is amount of the required cognitive capacity during performing tasks. Electroencephalogram (EEG) is an objective monitoring technique used to evaluate mental workload. In this study, feature extraction methods based on Katz’s fractal dimension (KFD) and Higuchi’s fractal dimension; and error correcting output coding (ECOC) are proposed to classify mental workload levels through EEG signals, which were recorded during performing of the simultaneous tasks. ECOC, which is a classifier combination technique proposed for multiclass classification problems, is employed to classify mental workload as low, moderate and high level. ECOC was created based on one vs. all approach, by using support vector machines (SVM), k nearest neighbourhood and quadratic discriminant analysis. The performance of the proposed method is evaluated on Simultaneous Task EEG Workload (STEW) dataset collected from 48 subjects. By using KFD and HFD with respectively, the classification accuracy was determined as %78.44 and %95.39; and Cohen’s Kappa value was determined as 0.52 ve 0.89. The results indicate that combination of HFD and SVM-ECOC is a successful method in the multiclass classification of mental workload.

Keywords:

electroencephalography (EEG), mental workload (MWL), Katz’s fractal dimension, Higuchi’s fractal dimension, error coding output codes (ECOC),

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