![]() The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. This research focuses on three main points. ![]() This paper presents a new method which extracts and selects features from multi-channel EEG signals. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. Electroencephalogram (EEG) signals are used broadly in the medical fields. ![]()
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