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Since EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is...
While magnetoencephalography (MEG) is widely used to identify spatial locations of brain activations associated with various tasks, classification of single trials in stimulus-locked experiments remains an open subject. Very significant single-trial classification results have been published using electroencephalogram (EEG) data, but in the MEG case, the weakness of the magnetic fields originating...
A robust method for removal of artifacts such as eye blinks and electrocardiogram (ECG) from the electroencephalograms (EEGs) has been developed in this paper. The proposed hybrid method fuses support vector machines (SVMs) based classification and blind source separation (BSS) based on independent component analysis (ICA). The carefully chosen features for the classifier mainly represent the data...
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