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BEKİRYAZICI, ŞULE

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BEKİRYAZICI

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ŞULE

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    Detection and analysis of driver fatigue stages with eeg signals
    (Pamukkale Univ, 2022-01-01) Eken, Recep; Yılmaz, Güneş; YILMAZ, GÜNEŞ; Demir, Ahmet; DEMİR, AHMET; Bekiryazıcı, Şule; BEKİRYAZICI, ŞULE; Coşkun, Oğuzhan; Mühendislik Fakültesi; Elektrik ve Elektronik Mühendisliği Bölümü; 0000-0003-1115-186X; 0000-0001-8972-1952; GZM-6710-2022; AAH-4177-2021; AAH-4182-2021
    Today, many people die in traffic accidents. Sleeplessness and fatigue of drivers are shown as the most important cause of traffic accidents. For this reason, research on driver performance analysis is of great importance. In this study, a system is designed to analyze driver fatigue using electroencephalography (EEG) data. As the data set, the EEG signals from sustained-attention driving task prepared by National Chiao Tung University have been used. The data set is divided into four classes to determine the driver's fatigue times and level. In order to determine the frequency ranges that occur during driver fatigue phases, EEG signals are filtered. Principal Component Analysis method has been used to reduce the size of the features matrix. With the Divide and Conquer algorithm, all combinations in which the four classes will be separated best are determined and classification has been done at each step using sub-classifiers. As sub-classifiers, k-Nearest Neighborhood, Support Vector Machines and Linear Discrimination Analysis algorithms are used. As a result of the study, the average classification successes are 87.9% for the k-Nearest Neighborhood algorithm, 88.5% for the Support Vector Machines algorithm and 81.6% for Linear Discrimination Analysis. The highest classification success has been achieved as 93.2% with the Support Vector Machines classifier, between 67.5-90 min. of driving at the 4th grade fatigue level.
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    Publication
    Feature selection and analysis EEG signals with sequential forward selection algorithm and different classifiers
    (Ieee, 2020-01-01) Bekiryazıcı, Şule; Demir, Ahmet; Yılmaz, Güneş; BEKİRYAZICI, ŞULE; DEMİR, AHMET; YILMAZ, GÜNEŞ; Mühendislik Fakültesi; Elektrik Elektronik Mühendisliği Bölumü; 0000-0003-1115-186X; 0000-0001-8972-1952; AAH-4182-2021; AAH-4177-2021; KPP-7123-2024
    In this study, we investigated the features that could best represent EEG signals for brain computer interface systems and classifier accuracy was compared using different classification methods. EEG signals data set were taken from "BCI II Competition". In this study, inadequate features that reduce classification accuracy were determined by using sequential forward selection algorithms and were extracted from real-dimensional feature matrix. The remaining active feature matrix and real-dimensional feature matrix were classified using k-nearest neighbor, subspace K-nearest neighbor, support vector machines, subspace discriminant and random forest decision tree algorithms. As a result of this study, the highest classification accuracy of real-dimensional feature matrix was obtained as 83.8% by random forest decision tree algorithm. In the other, the highest classification accuracy of dimention reductioned feature matrix with sequential forward selection algorthm was obtained as 96.4% by random forest decision tree algorithm.