Yazar "Erkaymaz, H." seçeneğine göre listele

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    Electrooculogram and diplopia controlled fuzzy direction detect system
    (IEEE Computer Society, 2014) Erkaymaz, H.; Orak, I.M.; Özer, M.
    Coding of the organ movements have been seen commonly in the hardware designing which is useful for humanity in scientific researches. 5 sense organs which reveal different data with cellular structures, is the most important focus point. Especially, Eye is known as photosensitive sensor organ of living creature. There is a potential difference between cornea and retina of eye. This potential difference is known as electrooculogram (EOG). Nowadays, EOG signals are commonly used in these hardware designs. Obtained potential differences from EOG signals have been converted coded directional movements. Thus, in this study, 4 basic direction movements have been tried to detect with a fuzzy controlled model. It is shown that the fuzzy controlled system as determine the direction can be used successfully. In addition, eye direction movements of squint person are obtained by the system. © 2014 IEEE.
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    EOG based intelligent direction detect system with pre-filtering algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2015) Erkaymaz, H.; Özer, M.; Kaya, C.; Orak, I.M.
    Nowadays, artificial movements have been obtained by utilizing other organs for paralyzed patients. Especially the usage of eye movements for giving message to outside world became popular as a scientific subject. In studies according to eye movements, the Electrooculogram (EOG) signal is used. In this study, the vertical and horizontal EOG signals taken from electrodes, placed around the eyes, have been modelled by using Artificial Neural Networks (ANN) which is one of artificial intelligent technique. The system can sense four main directions (Right, Left, Up and Down) at the same time it can also detect blinking movements. Firstly, the signals have been pre-filtered, amplified and classified by ANN. The performance of recommended model has been demonstrated by analyzing statistical accuracy and confusion matrix according to the features of obtained signal. It has been seen that eye movements can be successfully determined by designed model. © 2015 IEEE.