Browsing by Author "Ozerdem, Mehmet Sirac"

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Classification of EEG Records for the Cursor Movement with the Convolutional Neural Network
(IEEE, 2018) Turk, Omer; Ozerdem, Mehmet Sirac; Türk, Ömer; Ozyildirim, BM; Yildirim, T
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%.
Classification of Eeg Signals Using Hilbert-Huang Transform-Based Deep Neural Networks
(IEEE, 2019) Zan, Hasan; Yildiz, Abdulnasir; Ozerdem, Mehmet Sirac; Zan, Hasan
Epilepsy is one of the most common neurologic disease. Electroencephalogram (EEG) contains physiologic and pathological information related human nervous system. EEG signals used in this study are obtained from Bonn University, Department of Epileptology EEG database. Original database has five subsets (A, B, C, D, E). Data have been reorganized into three groups which are healthy (AB), interictal (CD) and ictal EEG signals. Furthermore, in order to examine effect of signal length on classification performance, three different lengths are used. Hilbert-Huang transform is applied to the signals and they are represented as image files. Then, generated images are fed into deep neural networks with five different structures for classification. Accuracy is calculated for all cases to asses performance of proposed method. it is clear that successful results could be obtained using Hilbert-Huang transform along with deep learning networks.
Determination Of Changes in Frequencies of EEG Signal in Eyes Open/Closed Duration
(IEEE, 2015) Turk, Omer; Ozerdem, Mehmet Sirac; Türk, Ömer
In this study, the changes of the power spectral density (PSD) in the EEG data during eyes-closed and eyes-open states were analyzed. In the analysis, the interval of dominant frequencies was roughly determined with different approaches. The EEG signal is separated into sub bands with wavelet transform (WT). The Welch method which is the one of the classical methods was used for PSD prediction and the Burg and Yule-Walker parametric methods were used also for PSD prediction of the EEG signal. It was observed that the alpha rhythm is dominant band in the eyes closed state compared to eyes open state.
MENTAL ACTIVITY DETECTION FROM EEG RECORDS USING LOCAL BINARY PATTERN METHOD
(IEEE, 2017) Turk, Omer; Ozerdem, Mehmet Sirac; Türk, Ömer
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.