Yazar "Eraldemir, Server Göksel" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    Channel selection from EEG signals and application of support vector machine on EEG data
    (Institute of Electrical and Electronics Engineers Inc., 2017) Arslan, Mustafa Turan; Eraldemir, Server Göksel; Yildirim, Esen
    In this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for choosing the most effective channels. Decreasing the number of channels without compromising the accuracy, is an important issue for real time applications for which a short analysis time is crucial. In this study, mental arithmetic and silent reading tasks are classified with an accuracy of 90.71%, a precision rate of 91.03% and F-measure rate of 90.63% on the average using 26 channels, whereas the accuracy, precision and F-measure were 90.44%, 90.61% and 90.08, respectively which were comparable to that of obtained using all channels, for reduced number of channels. © 2017 IEEE.
  • [ X ]
    Öğe
    Classification of EEG Signals in DepressedPatients
    (2020) Eraldemir, Server Göksel; Kılıç, Ümıt; Keleş, Mümıne Kaya; Demirkol, Mehmet Emin; Yıldırım, Esen; Tamam, Lut
    Electroencephalography (EEG) are electrical signals that occur in every activity of the brain. Investigation of normal and abnormal changes that take place in the human brain using EEG signals is a widely used method in recent years. The World Health Organization (WHO) states that one of the most important health problems in today's society is depressive disorders. Nowadays, various scales are used in the diagnosis of depressive disorder in individuals. These scales are based on the declaration of the individual. In recent studies, EEG has been used as a biomarker for the diagnosis of depression. In this study, EEG signals from 30 patients with clinical depressive disorder have been recorded. EEG signals have been collected for 1 minute with eyes open and closed. The collected data have been divided into attributes by continuous wavelet transform which is used in many studies in processing non-stationary signals such as EEG. Obtained attributes have been classified with kNN classification method. As a result, it was observed that EEG signals, collected from subjects with depression while eyes are open and closed, can be classified with an accuracy of 91.30%.
  • [ X ]
    Öğe
    Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks
    (2017) Arslan, Mustafa Turan; Eraldemir, Server Göksel; Yıldırım, Esen
    In this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subjectdependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis