Seyed Navid Resalat

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The objective of this study is development of driver's sleepiness using Visually Evoked Potentials (VEP). VEP computed from EEG signals from the visual cortex. We use the Steady State VEPs (SSVEPs) that are one of the most important EEG signals used in human computer interface systems. SSVEP is a response to visual stimuli presented. We present a(More)
Brain Computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important.(More)
Steady-State Visual Evoked Potentials (SSVEPs) are one of the most important EEG signals used in Human Computer Interface (HCI) systems. These signals are generated by Looking at flickering external light sources stimulating the central part of the retina. By increasing the number of external light sources, detection of the corresponding SSVEPs from the(More)
A novel EEG-based system for driver's sleepiness detection is proposed. Driver's sleepiness is an important factor in many accidents. Therefore, real-time sleepiness detection can restrain accidents effectively. In this study, SSVEPs are used for running the proposed system. In order to generate SSVEPs in the brain activities, two experimental setups(More)
INTRODUCTION Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications. METHODS This study applied wide variety of features on the recorded data using Linear(More)
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