Ozetge-Bu �ah�mada, altl temel yiiz ifadesini tespit i�in iki boyutlu Gorgiil Kip AYrl�lmml (2B-GKA) kullanan bir yontem sunulmaktadlr. Duygu durumunu temsil eden yiiz imgelerinden iist ve alt yiiz bOigeleri klrpllarak 2B-GKA uygulanml� ve her bir bOigeye kar�lhk ii� Ozgiil Kip Fonksiyonu (OKF) ile bir artlk imge elde edilmi�tir. Yiiz ifadesini temsil eden sekiz alt imgede etkili frekans bolgesini tespit etmek amaclyla fark!! bant geni�liklerine sahip siizge�ler tasarlanml� ve etkileri gozlemlenmi�tir. Smltlandlrma i�in ozniteliklerin elde edilmesinde alt imgelerden hesaplanan istatistiksel metrikler ve ikinci yakla�lm olarak Radon Donii�iimii kullamlml�tlr. Olu�turulan nitelik vektOrleri Destek Vektor Makineleri ile slmtlandlrllml�tlr. iki yakla�lm i�in bulunan sonu�lar kar�lla�tlrllml�tlr. Anahtar Kelimeler -yuz ijadeleri; eylem birimleri; gorgUl kip ayrl §lml; radon donu §umU. Abstract-In this study, we present a method to estimate six basic facial expressions of a person by applying 2dimensional Empirical Mode Decomposition (2D-EMD) on face images which represent the emotional state. Three intrinsic mode functions (IMF) and a residue is obtained by applying 2D-EMD to each facial regions that are cropped from upper and lower face regions. Filters having different bandwidths are designed to detect the efficient frequency band of all eight sub-images. Two approaches are used to calculate the features: statistical metrics of each sub-image, and Radon Transform of sub-images are used as features in classification. Support Vector Machines classifier is used as to identify the facial expression. The results for the two feature vector approaches are presented and compared.

In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD-and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.

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