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Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in...
In Brain Computer Interface (BCI), the thoughts of a subject is read to provide an appropriate way of communication using only brain signals. The Information of electroencephalogram (EEG) signals defer between subjects depending on their thoughts according to research. In this paper, a comparison between different types of features tested by several classifiers is done to propose a model for classifying...
Epilepsy is a neurological disorder disease that affects more than 55 million people in the world. In this paper, we have proposed an efficient intelligent pattern recognition system for the classification of epileptic and non-epileptic electroencephalogram (EEG) signals. For this purpose, we used state-of-the-art machine learning technique, i.e., SVM (support vector machines) to classify epileptic...
In this study, we try to find a method to investigate the relationship between stair's slope and ascent/descent gaits in stairs. With the method, we can speculate the stair slope of a particular stair from a subject's gait so that we can use it to be an initial parameter for estimating the vertical distance the subject has climbed. In this way, this study could provide a practical method to realize...
In this paper the problematic of epileptic detection is treated. An algorithm of EEG signal classification into two classes: Healthy and Epileptics is developed. The difference with conventional methods is the use of free seizure epileptic records. A good classification accuracy means that it is possible to detect an epileptic in normal state or at an early stage of epilepsy. The raw EEG signal is...
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