New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot

ÇİNAR E., Sahin F.

NEURAL COMPUTING & APPLICATIONS, cilt.22, sa.1, ss.29-39, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 1
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s00521-011-0744-x
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.29-39
  • Anahtar Kelimeler: Brain-computer interface, Classification algorithms, FFSVC, IFFSVC, PSO-RBFN, Particle swarm optimization, Clustering, PARTICLE SWARM, FUZZY, RECOGNITION, PERFORMANCE, SYSTEM
  • Eskişehir Osmangazi Üniversitesi Adresli: Hayır

Özet

This paper studies the state-of-the-art classification techniques for electroencephalogram (EEG) signals. Fuzzy Functions Support Vector Classifier, Improved Fuzzy Functions Support Vector Classifier and a novel technique that has been designed by utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performances of the techniques are compared on standard EEG datasets that are publicly available and used by brain-computer interface (BCI) researchers. In addition to the standard EEG datasets, the proposed classifier is also tested on non-EEG datasets for thorough comparison. Within the scope of this study, several data clustering algorithms such as Fuzzy C-means, K-means and PSO clustering algorithms are studied and their clustering performances on the same datasets are compared. The results show that PSO-RBFN might reach the classification performance of state-of-the art classifiers and might be a better alternative technique in the classification of EEG signals for real-time application. This has been demonstrated by implementing the proposed classifier in a real-time BCI application for a mobile robot control.