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    Classification of Electrocorticography signals reduced by Wavelet Transform
    (Institute of Electrical and Electronics Engineers Inc., 2016) Kurnaz, I.; Erkan, E.
    Studies to solve the mystery of how the human brain works is receiving considerable attention in recent years. Analysis of the signals produced in brain is also within the scope. In this study, classification of ECoG (Electrocorticography) signals which produced in brain is performed. The data used in this study were obtained from data set no 1 which had been used on BCI Competition III. The first part, to decrease the processing load, the number of channels are reduced by eliminating channels (electrodes) which have low separation success. Than it was obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet Coefficents. These features are tested by KNN (K Nearest Neighbors), SVM (Support Vector Machine) and LDA (Linear Discriminate Analysis) classification methods. It's obtained that 94% success in classification by using KNN. © 2016 IEEE.
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    Gesture recognition using SAX method
    (Institute of Electrical and Electronics Engineers Inc., 2016) Kurnaz, I.; Durgut, R.
    In this study, an application is developed to recognize human gestures using data which was recorded by using Microsoft Kinect. The data set used in the study is MSRC-12, and it is created by Microsoft. It has several daily human gestures which were recorded from different users. Before gesture recognition process, recorded data was reduced by PAA method and then it was classified by SAX method. Symbols (which are generated by SAX) of percentage similarity is calculated by developed algorithm. The application can recognize all human gestures in dataset correctly. © 2016 IEEE.