A study of recent classification algorithms and a novel approach for EEG data classification

This paper analyzes the application of different classification techniques for Electroencephalography (EEG) signals. Fuzzy Functions Support Vector Classifier (FFSVC), Improved Fuzzy Functions Support Vector Classifier (IFFSVC) and a novel hybrid technique that has been designed utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performance of the techniques is compared on the same standard datasets that are publicly available and used by many Brain Computer Interface (BCI) researchers. Results show that proposed classifiers might reach the classification performance of state of the art classifiers and might be used as alternative techniques in the classification applications of EEG signals.