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Autoencoders Based Deep Learning Approach for Focal-Nonfocal EEG Classification Problem

EEG markers are the records of brain electrical activity which gives meaningful notice about individuals status. Detection of neurological diseases is only possible with effective analysis of EEG records. Epilepsy is a such neurological disease that has been a serious health problem affects life quality of human being. EEG based epilepsy detection in an effective and reliable way is a crucial issue for researchers. Effective feature extraction techniques to diminish input vector is a significant point in applications. In this study, auto-encoder based unsupervised feature extraction method was used and a deep learning approach was investigated to classify focal-non-focal EEG records. Proposed work has superiority in contrast to conventional methods because dataset was classified without using pre-processing and dimensionality-reduction methods. It has been thought that this work proposes an effective approach to diagnose epilepsy by using deep neural networks.