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Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Gokhan ALTAN [1] , Yakup KUTLU [2] , Novruz ALLAHVERDİ [3]

Abstract

An 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.

Keywords

Deep Belief Networks, SCP, Slow Cortical potentials, Hilbert-Huang Transform, EEG

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Details

Subjects Engineering
Journal Section Research Article
Authors

Orcid: orcid.org/0000-0001-7883-3131
Author: Gokhan ALTAN
Institution: MUSTAFA KEMAL ÜNİVERSİTESİ
Country: Turkey

Author: Yakup KUTLU
Institution: İSKENDERUN TEKNİK ÜNİVERSİTESİ
Country: Turkey

Author: Novruz ALLAHVERDİ
Institution: KTO KARATAY ÜNİVERSİTESİ
Country: Turkey
Dates

Publication Date : December 1, 2016

Cite

Bibtex @research article { ijamec270307, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {2147-8228}, eissn = {2147-8228}, address = {}, publisher = {Selcuk University}, year = {2016}, volume = {}, pages = {205 - 210}, doi = {10.18100/ijamec.270307}, title = {Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke}, key = {cite}, author = {Altan, Gokhan and Kutlu, Yakup and Allahverdi̇, Novruz} }
APA Altan, G , Kutlu, Y , Allahverdi̇, N . (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke . International Journal of Applied Mathematics Electronics and Computers , Special Issue (2016) , 205-210 . DOI: 10.18100/ijamec.270307
MLA Altan, G , Kutlu, Y , Allahverdi̇, N . "Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke" . International Journal of Applied Mathematics Electronics and Computers (2016 ): 205-210 <https://dergipark.org.tr/en/pub/ijamec/issue/25619/270307>
Chicago Altan, G , Kutlu, Y , Allahverdi̇, N . "Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke". International Journal of Applied Mathematics Electronics and Computers (2016 ): 205-210
RIS TY - JOUR T1 - Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke AU - Gokhan Altan , Yakup Kutlu , Novruz Allahverdi̇ Y1 - 2016 PY - 2016 N1 - doi: 10.18100/ijamec.270307 DO - 10.18100/ijamec.270307 T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 205 EP - 210 VL - IS - Special Issue-1 SN - 2147-8228-2147-8228 M3 - doi: 10.18100/ijamec.270307 UR - https://doi.org/10.18100/ijamec.270307 Y2 - 2016 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke %A Gokhan Altan , Yakup Kutlu , Novruz Allahverdi̇ %T Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke %D 2016 %J International Journal of Applied Mathematics Electronics and Computers %P 2147-8228-2147-8228 %V %N Special Issue-1 %R doi: 10.18100/ijamec.270307 %U 10.18100/ijamec.270307
ISNAD Altan, Gokhan , Kutlu, Yakup , Allahverdi̇, Novruz . "Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke". International Journal of Applied Mathematics Electronics and Computers / Special Issue-1 (December 2016): 205-210 . https://doi.org/10.18100/ijamec.270307
AMA Altan G , Kutlu Y , Allahverdi̇ N . Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 205-210.
Vancouver Altan G , Kutlu Y , Allahverdi̇ N . Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 205-210.
IEEE G. Altan , Y. Kutlu and N. Allahverdi̇ , "Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke", International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, pp. 205-210, Dec. 2016, doi:10.18100/ijamec.270307
Authors of the Article
Gokhan ALTAN [1]
Yakup KUTLU [2]
Novruz ALLAHVERDİ [3]
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