NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces

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dc.authorid0000-0001-9092-3631en_US
dc.authorid0000-0003-2863-2758en_US
dc.contributor.authorArı, Emre
dc.contributor.authorTaçgın, Ertuğrul
dc.date.accessioned2024-04-17T10:56:40Z
dc.date.available2024-04-17T10:56:40Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractObjective: Brain Computer Interface (BCI) systems have been developed to identify and classify brain signals and integrate them into a control system. Even though many different methods and models have been developed for the brain signals classification, the majority of these studies have emerged as specialized models. In addition, preprocessing and signal preprocessing methods which are largely based on human knowledge and experience have been used extensively for classification models. These methods degrade the performance of real-time BCI systems and require great time and effort to design and implement the right method. Approach: In order to eliminate these disadvantages, we developed a generalized and robust CNN model called as No-Filter EEG (NF-EEG) to classify multi class motor imagery brain signals with raw data and without applying any signal preprocessing methods. In an attempt to increase the speed and success of this developed model, input reshaping has been made and various data augmentation methods have been applied to the data. Main results: Compared to many other state-of-the-art models, NF-EEG outperformed leading state-of-the-art models in two most used motor imagery datasets and achieved 93.56% in the two-class BCI-IV-2A dataset and 88.40% in the two-class BCI-IV-2B dataset and 81.05% accuracy in the classification of four-class BCI-IV-2A dataset. Significance: This proposed method has emerged as a generalized model without signal preprocessing and it greatly reduces the time and effort required for preparation for classification, prevents human-induced errors on the data, presents very effective input reshaping, and also increases the classification accuracy.en_US
dc.identifier.citationArı, E. ve Taçgın, E. (2024). NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces. Biomedical Signal Processing and Control, 92, 1-15.en_US
dc.identifier.doi10.1016/j.bspc.2024.106081en_US
dc.identifier.endpage15en_US
dc.identifier.issn1746-8094
dc.identifier.scopus2-s2.0-85184747053en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1746809424001393?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/11468/13903
dc.identifier.volume92en_US
dc.identifier.wosWOS:001180928400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArı, Emre
dc.institutionauthorTaçgın, Ertuğrul
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain Computer Interface (BCI)en_US
dc.subjectClassificationen_US
dc.subjectData augmentationen_US
dc.subjectDeep learningen_US
dc.subjectEEG motor imageryen_US
dc.subjectInput reshapingen_US
dc.titleNF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfacesen_US
dc.typeArticleen_US

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