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      İki durumlu bir beyin bilgisayar arayüzünde özellik çıkarımı ve sınıflandırma

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      Author(s)
      Altındiş, Fatih
      Yılmaz, B.
      Date
      2017-10
      Source Title
      2016 Medical Technologies National Conference, TIPTEKNO 2016
      Publisher
      IEEE
      Pages
      1 - 4
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Beyin bilgisayar arayüzü (BBA) teknolojisi motor nöronlarının özelliğini kaybeden ve hareket kabiliyeti kısıtlanmış ALS ve felçli hastalar gibi birçok kişinin dış dünya ile iletişimini sağlamaya yönelik kullanılmaktadır. Bu çalışmada, Avusturya’daki Graz Üniversitesi’nde alınmış EEG veri seti kullanılarak gerçek zamanlı EEG işleme simülasyonu ile motor hayal etme sınıflandırılması amaçlanmıştır. Bu veri setinde sağ el ya da sol elin hareket ettirilme hayali esnasında 8 kişiden alınmış iki kanallı EEG sinyalleri bulunmaktadır. Her katılımcıdan 60 sağ ve 60 sol olmak üzere toplamda 120 adet yaklaşık 9 saniyelik motor hayal etme deneme sinyali kayıt edilmiştir. Bu sinyaller filtrelemeye tabi tutulmuştur. Yirmi dört, 32 ve 40 elemanlı özellik vektörü bant geçiren filtreler kullanarak elde edilen göreceli güç değişim değerleridir (GGDD). Bu çalışmada, lineer diskriminant analizi (LDA), k en yakın komşular (KNN) ve destek vektör makinaları (SVM) ile sınıflandırma yapılmış, en iyi sınıflandırma performansının 24 değerli özellik vektörüyle ve LDA sınıflandırma yöntemiyle elde edildiği gösterilmiştir.
       
      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. © 2016 IEEE.
      Keywords
      Brain-computer interfaces
      Classification
      EEG
      Motor imagery
      Relative band power
      Biomedical engineering
      Brain computer interface
      Classification (of information)
      Discriminant analysis
      Electroencephalography
      Feature extraction
      Image retrieval
      Interface states
      Interfaces (computer)
      Medical computing
      Nearest neighbor search
      Neurons
      Signal processing
      Support vector machines
      Vectors
      Classification methods
      Feature extraction and classification
      Feature vectors
      K nearest neighbor (KNN)
      Linear discriminant analysis
      Motor imagery
      Paralyzed patients
      Biomedical signal processing
      Permalink
      http://hdl.handle.net/11693/37572
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TIPTEKNO.2016.7863118
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      • Department of Electrical and Electronics Engineering 3868
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