Publications by authors named "Oguz Bayat"

Article Synopsis
  • * This study introduces a novel method for classifying schizophrenia using EEG signals from the brain, with data collected from 28 subjects, analyzing brain activity across different frequency bands and applying various techniques to enhance signal clarity.
  • * The findings indicate that the Support Vector Machine (SVM) algorithm, particularly with Log Energy entropy features in a 1-second window, performed the best in classifying schizophrenia, highlighting the importance of feature selection in achieving accurate diagnoses.
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Wireless actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring.

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There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer's disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer's patients.

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An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal.

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Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures.

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