Browsing by Author "Özerdem M.S."

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    Classification of EEG Records for the Cursor Movement with the Convolutional Neural Network [Imleç Hareketine ilişkin EEG Kayitlarinin Evrişimsel Sinir Agi ile Siniflandirilmasi]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Türk O.; Özerdem M.S.
    Nowadays, very successful results are obtained with deep learning architectures which can be applied to many fields. Because of the high performances it provides in many areas, deep learning has come to a central position in machine learning and pattern recognition. In this study, electroencephalogram (EEG) signals related to up and down cursor movements were represented as image pattern by using obtained approximation coefficients after wavelet transform. The Obtained image patterns were classified by applying Convolutional Neural Network. In this study, EEG records related to cursor movements were classified and classification accuracy was obtained as 88.13%. © 2018 IEEE.
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    Citation - Scopus: 2
    Mental activity detection from EEG records using local binary pattern method [Yerel ikili örüntü yöntemi kullanarak EEG kayitlarindan mental aktivite tespiti]
    (Institute of Electrical and Electronics Engineers Inc., 2017) Türk Ö.; Özerdem M.S.
    Electroencephalogram signals are widely used in the detection of different activities but not in the desired level. In this study with this motivation, it is aimed to obtain the attributes by using the Local Bilinear Pattern (LBP) method of EEG records for various mental activities and to classify these features by k-Nearest Neighbor (k-NN) method. The binary classification performance of these EEG records containing 5 mental tasks was evaluated. In addition, in order to evaluate classification performance, confusion matrix was used as model performance criterion. In the study, the average of the classification performance of all participants was found as 87.38%. As a model performance criterion from the participants' classification of mental activity, accuracy was 85.03%, precision was 85.40% and sensitivity was 85.47%. So, as a result the obtained results support the literature and the applicability of the LBP method for EEG markings has been confirmed. © 2017 IEEE.