Scopus
YÖKSİS DOI Eşleşti
SJR Q3
Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN
Australasian Physical and Engineering Sciences in Medicine · Eylül 2016
YÖKSİS Kayıtları
Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2 D cursor movements for BCI using SVM and ANN
Australasian Physical Engineering Sciences in Medicine · 2016 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 TarihiEylül 2016
Cilt / Sayfa39 · 665-676
Scopus ID2-s2.0-84976878205
Özet
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha–beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.
Yazarlar (3)
1
Muhammet Serdar Bascil
2
Ahmet Y. Tesneli
3
Feyzullah Temurtas
Anahtar Kelimeler
Brain computer interface (BCI)
EEG
ICA
k-fold cross validation
LS-SVM
LVQ
MLNN
PCA
PNN
PSD
SVM
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
46
Atıf
3
Yazar
11
Anahtar Kelime