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EEG-based emotion recognition is a relatively new research field in the human computer interaction area and its aim is the implementation of new algorithms that would identify and recognize emotions from EEG (electroencephalogram) signals. Towards that, a novel method is presented in this paper that employs an optimized hybrid filter, using empirical mode decomposition (EMD) and genetic algorithms...
Support vector machine (SVM) is a machine learning technique widely applied in classification problems. SVM are based on the Vapnik's Statistical Learning Theory, and successively extended by a number of researchers. On the order hand, the electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. In...
The availability of quantitative biological markers that are correlated with qualitative psychiatric phenotypes helps us utilize automatic methods to diagnose and classify these phenotypes. EEG signals are appropriate means for extraction of these quantitative markers. According to the literature, many brain disorders and/or mental tasks can be detected by analyzing EEG signals. One such a psychiatric...
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