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dc.contributor.authorAllahverdi, Novruz
dc.contributor.authorAltan, Gökhan
dc.contributor.authorKutlu, Yakup
dc.date.accessioned2019-07-10T07:54:36Z
dc.date.available2019-07-10T07:54:36Z
dc.date.issued2016-09-03
dc.identifier.issn2147-8228
dc.identifier.urihttps://hdl.handle.net/20.500.12498/993
dc.description.abstractAn electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a low pass filter. Hilbert-Huang Transform is applied to the trails and various numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics and standard statistics are extracted from IMFs to create the dataset. The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.en_US
dc.language.isoenen_US
dc.subjectDeep Belief Networken_US
dc.subjectSlow Cortical potentials,en_US
dc.subjectHilbert-Huang Transformen_US
dc.titleDeep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Strokeen_US
dc.typeArticleen_US


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