Scopus
YÖKSİS DOI Eşleşti
SJR Q3
Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface
Australasian Physical and Engineering Sciences in Medicine · Haziran 2015
YÖKSİS Kayıtları
Multi channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1 D cursor movement for brain computer interface
Australasian Physical & Engineering Sciences in Medicine · 2015 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Multi channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1 D cursor movement for brain computer interface
Australasian Physical Engineering Sciences in Medicine · 2015 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Multi channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1 D cursor movement for brain computer interface
2015 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2 D cursor movements for BCI using SVM and ANN
2016 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Glossokinetic potential based tongue–machine interface for 1-D extraction
2018 ISSN: 0158-9938 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Makale Bilgileri
ISSN01589938
Yayın TarihiHaziran 2015
Cilt / Sayfa38 · 229-239
Scopus ID2-s2.0-84943363244
Özet
Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha & beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.
Yazarlar (3)
1
Muhammet Serdar Bascil
2
Ahmet Y. Tesneli
3
Feyzullah Temurtas
Anahtar Kelimeler
Average signal power
Brain computer interface (BCI)
EEG
K-Fold cross validation
LVQ
MLNN
PCA
PNN
Kurumlar
Bozok Üniversitesi
Yozgat Turkey
Sakarya Üniversitesi
Serdivan Turkey
Scimago Dergi (ISSN Eşleşmesi)
Australasian Physical and Engineering Sciences in Medicine
Q3
SJR Skoru0,329
ÜlkeNetherlands
Biomedical Engineering (Q3)
Biophysics (Q3)
Physics and Astronomy (miscellaneous) (Q3)
Radiology, Nuclear Medicine and Imaging (Q3)
Metrikler
31
Atıf
3
Yazar
8
Anahtar Kelime