— In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attentions. This study tries to develop a classifier, which can provide higher classification accuracy for multiclass SSVEP data. Four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 second window and features were extracted using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass Support Vector Machines (SVM) is compared with Artificial Neural Network (ANN) models on the basis of SSVEP classifier accuracies. OAA SVM classifier had got an average accuracy of 88.55% for SSVEP classification over 10 subjects. Based on this study, it is found that for SSVEP classification OAA -SVM classifier can provide better results than ANN.
— Steady-state visual evoked potential, brain computer interface, artificial neural network, supprot vector machine.
The authors are with the Instrumentation and Control Engineering Department, National Institute of Technology, Jalandhar, PIN 144011, India (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Rajesh Singla and Haseena B. A., " Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications," International Journal of Information and Electronics Engineering vol. 4, no. 1, pp. 6-10, 2014.