Yerel ikili örüntü yöntemi kullanarak EEG kayıtlarından mental aktivite tespiti
Yükleniyor...
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Electroencephalogram signals are widely used in the detection of different activities but not in the desired level. In this study with this motivation, it is aimed to obtain the attributes by using the Local Bilinear Pattern (LBP) method of EEG records for various mental activities and to classify these features by k-Nearest Neighbor (k-NN) method. The binary classification performance of these EEG records containing 5 mental tasks was evaluated. In addition, in order to evaluate classification performance, confusion matrix was used as model performance criterion. In the study, the average of the classification performance of all participants was found as 87.38%. As a model performance criterion from the participants' classification of mental activity, accuracy was 85.03%, precision was 85.40% and sensitivity was 85.47%. So, as a result the obtained results support the literature and the applicability of the LBP method for EEG markings has been confirmed.
Açıklama
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017 -- -- 115012
Anahtar Kelimeler
K-Nn, Local binary pattern, Mental activities
Kaynak
IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Türk, Ö. ve Özerdem, M. S. (2017). Yerel ikili örüntü yöntemi kullanarak EEG kayıtlarından mental aktivite tespiti. IDAP 2017 - International Artificial Intelligence and Data Processing Symposium.
