Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes
Sakarya University Journal of Science · Nisan 2023
YÖKSİS Kayıtları
Feature Analysis for Motor Imagery EEG Signals with Different Classification Schemes
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi · 2023 TR DİZİN
DOKTOR ÖĞRETİM ÜYESİ ESRA KAYA →
Feature Analysis For Motor Imagery EEG Signals With Different Classification Schemes
Sakarya University Journal of Science · 2023 Academic search Premier
PROFESÖR İSMAİL SARITAŞ →
Makale Bilgileri
DergiSakarya University Journal of Science
Yayın TarihiNisan 2023
Cilt / Sayfa27 · 259-270
Scopus ID2-s2.0-85218134794
Erişim🔓 Açık Erişim
Özet
A Brain-Computer Interface (BCI) is a communication system that decodes and transfers information directly from the brain to external devices. The electroencephalogram (EEG) technique is used to measure the electrical signals corresponding to commands occurring in the brain to control functions. The signals used for control applications in BCI are called Motor Imagery (MI) EEG signals. EEG signals are noisy, so it is important to use the right methods to recognize patterns correctly. This study examined the performances of different classification schemes to train networks using Ensemble Subspace Discriminant classifier. Also, the most efficient feature space was found using Neighborhood Component Analysis. The maximum average accuracy in classifying MI signals corresponding to right-direction and left-direction was 80.4% with a subject-specific classification scheme and 250 features.
Yazarlar (2)
1
Esra Kaya
ORCID: 0000-0003-1401-9071
2
Ismail Saritas
Anahtar Kelimeler
BCI
Classification scheme
Eeg
Feature selection
Subject-independent
Subject-specific
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey