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dc.contributor.authorNour, Majid
dc.contributor.authorOzturk, Saban
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-03-12T19:28:51Z
dc.date.available2024-03-12T19:28:51Z
dc.date.issued2021
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06202-4
dc.identifier.urihttps://hdl.handle.net/20.500.12450/2073
dc.description.abstractThe most effective way to communicate between the brain and electronic devices in the outside world is the brain-computer interface (BCI) systems. BCI systems use signals of being through neural activity in the brain to fulfill this function. Traditional BCI systems use electroencephalography (E.E.G.) signals due to their characteristics, such as temporal resolution, cost, and noninvasive nature. However, the inherent complex features make the analysis process very difficult. In addition, its sensitivity to internal and external noise affects performance negatively. Near-infrared spectroscopy (NIRS), which describes brain hemodynamics, is a noninvasive method and robust against the problems that E.E.G. suffers. We present an effective study examining the effects of E.E.G. and NIRS signals for BCI and investigating the contribution of their combination to performance. Also, a novel classification framework using multiple bandwidth method with optimized convolution neural network (CNN) is proposed. The proposed method classifies the recorded E.E.G. and NIRS signals according to the imagination of opening and closing the subjects' right and left hands. A CNN architecture including fully connected layer optimization using E.E.G. and NIRS signals is trained in an end-to-end manner. Instead of using a single bandwidth as in the literature, multiple bandwidths are used in the training process. In this way, information loss in band filtering tasks is prevented. Performance indicators obtained from experiments performed using the proposed framework are superior to current state-of-the-art methods in the literature in the most significant performance metrics: accuracy and stability. The proposed approach has a higher classification performance than current state-of-the-art methods, with an accuracy performance of 99.85%. On the other hand, in order to test the performance of the proposed CNN method, a detailed ablation study section on single-band experiments and including analysis of each component is presented.en_US
dc.language.isoengen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBCIen_US
dc.subjectEEGen_US
dc.subjectNIRSen_US
dc.subjectCNNen_US
dc.subjectClassificationen_US
dc.subjectFeature extractionen_US
dc.titleA novel classification framework using multiple bandwidth method with optimized CNN for brain-computer interfaces with EEG-fNIRS signalsen_US
dc.typearticleen_US
dc.departmentAmasya Üniversitesien_US
dc.authoridPolat, Kemal/0000-0003-1840-9958
dc.authoridÖztürk, Şaban/0000-0003-2371-8173
dc.authoridNour, Majid/0000-0001-8461-1404
dc.identifier.volume33en_US
dc.identifier.issue22en_US
dc.identifier.startpage15815en_US
dc.identifier.endpage15829en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-85110262099en_US
dc.identifier.doi10.1007/s00521-021-06202-4
dc.department-temp[Nour, Majid] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia; [Ozturk, Saban] Amasya Univ, Technol Fac, Dept Elect & Elect Engn, TR-05100 Amasya, Turkey; [Polat, Kemal] Abant Izzet Baysal Univ, Fac Engn, Dept Elect & Elect Engn, Bolu, Turkeyen_US
dc.identifier.wosWOS:000671520500004en_US
dc.authorwosidPolat, Kemal/AGZ-2143-2022
dc.authorwosidÖztürk, Şaban/ABI-3936-2020
dc.authorwosidNour, Majid/D-9224-2018


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