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The discrimination of the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated...
Among the various frameworks in which electroencephalographic (EEG) signal synchronization has been traditionally formulated, the most widely studied and used is the coherence that is entirely based on frequency analysis. However, at present time it is possible to capture information about the temporal profile of coherence, which is particularly helpful in studying non-stationary time-varying brain...
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