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dc.contributor.authorErgun, E.
dc.contributor.authorAydemir, O.
dc.date.accessioned2020-12-19T20:17:31Z
dc.date.available2020-12-19T20:17:31Z
dc.date.issued2018
dc.identifier.isbn9.78154E+12
dc.identifier.urihttps://doi.org/10.1109/UBMK.2018.8566462
dc.identifier.urihttps://hdl.handle.net/11436/4402
dc.description3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 20 September 2018 through 23 September 2018 -- -- 143560en_US
dc.description.abstractThere has been an increase interest for functional near-infrared spectroscopy (NIRS) in recent years since it is a non-invasive technique as well as few restrictions to the subjects and not affected by electrical noise. In this study, we analyzed mental arithmetic based NIRS signals that it can be helpful for patients like dyscalculia where difficulty learning or lack of attention problem exists. So, it is important that the mental arithmetic is effectively separated from NIRS signal. For this purpose, first, we determined change in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations by applying the modified Beer-Lambert law to NIRS data set. After Hilbert transform (HT)+ sum derivative (SD) based features were extracted from pre-processed HbR and HbO, these features were classified by k-nearest neighbors. The average classification accuracy (CA) rates of 82.87% and 84.94% were calculated from the HT+SD based features that best determine the mental arithmetic of the HbR and HbO signals, respectively. It can be said that the proposed method is effective for this dataset, in view of the fact that these values are 2.17% and 1.34% higher than CAs calculated in the literature for HbR and HbO, respectively. © 2018 IEEE.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHilbert transformen_US
dc.subjectk-Nearest neighborhooden_US
dc.subjectmental arithmeticen_US
dc.subjectNear-Infrared spectroscopyen_US
dc.subjectsum derivativeen_US
dc.titleDecoding of Binary Mental Arithmetic Based Near-Infrared Spectroscopy Signalsen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentRTEÜen_US
dc.identifier.doi10.1109/UBMK.2018.8566462
dc.identifier.startpage201en_US
dc.identifier.endpage204en_US
dc.relation.journalUBMK 2018 - 3rd International Conference on Computer Science and Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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