WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Author: search.filters.author.Altindis, FatihScopus Q: search.filters.scopusQuartile.N/A

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  • Conference Object
    Citation - Scopus: 1
    Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals
    (IEEE, 2021-11-04) Altindis, Fatih; Yilmaz, Bulent
    Detection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.
  • Conference Object
    Citation - Scopus: 3
    İki Durumlu Bir Beyin Bilgisayar Arayüzünde Özellik Çıkarımı ve Sınıflandırma
    (Institute of Electrical and Electronics Engineers Inc., 2016-10) Altindis, Fatih; Yilmaz, Bulent
    Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2017 Elsevier B.V., All rights reserved.