Discrimination of Rest, Motor Imagery and Movement for Brain-Computer Interface Applications

dc.contributor.author Ozturk, Nedime
dc.contributor.author Yilmaz, Bulent
dc.contributor.department AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü en_US
dc.date.accessioned 2021-05-24T08:40:18Z
dc.date.available 2021-05-24T08:40:18Z
dc.date.issued 2018 en_US
dc.description.abstract Brain-computer interface (BCI) is a system that provides a means to control prosthesis, wheelchair, or similar devices using brain waves without direct motor nervous system involvement. For this purpose, brain waves obtained from multiple electrodes placed on the scalp (EEG, Electroencephalogram) are used. Emotiv Epoc used to obtain EEG signals is a low-cost device and has real-time applications.. The aim of this study is the detection of rest, imagination and real movement using EEG signals obtained by Emotiv Epoc headset. As a result, As a result, the data obtained from 39 trials from a female subject were classified resting, motion imagination and movement, according to 97.4% accuracy by using the statistical features of distortion, logarithm energy entropy, energy, Shannon entropy and kurtosis.In this study, it has been shown that this system can be remarkably successful for BCI applications. en_US
dc.description.sponsorship Biyomedikal Klinik Muhendisligi Dernegi; Izmir Katip Celebi Univ, Biyomedikal Muhendisligi Bolumu en_US
dc.identifier.isbn 978-1-5386-6852-8
dc.identifier.uri https://hdl.handle.net/20.500.12573/738
dc.language.iso tur en_US
dc.publisher IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA en_US
dc.relation.journal 2018 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO) en_US
dc.relation.publicationcategory Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject EEG signal processing en_US
dc.subject first-order statistics features en_US
dc.subject motor imagery en_US
dc.subject EEG en_US
dc.subject Brain Computer Interface en_US
dc.title Discrimination of Rest, Motor Imagery and Movement for Brain-Computer Interface Applications en_US
dc.type conferenceObject en_US

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