Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)

Poznan University of Technology, Institute of Mechanical Technology, Skłodowska-Curie Square 5, 60-965 Poznań, Poland

^* Corresponding author: arkadiusz.j.kubacki@doctorate.put.poznan.pl

Abstract

The paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been checked: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and one based on Artificial Neural Network (ANN). First part is concentrated on brain-computer interfaces and classification of them. The second part describes algorithms of all using classifiers. In the next part, authors present test stand and how the experiment is built. The last part consists of results of these tests. The best was the classifier based on Artificial Neural Network – up to 95% of correct identified. The worst results were obtained from Support Vector Machine – about 70%.