Comparative study of motor imagery classification based on BP-NN and SVM

Abstract

Motor imagery (MI) classification based on electroencephalogram (EEG) with high speed and accuracy is a key issue in brain–computer-interface (BCI) technology. This study compared the support vector machine (SVM) and back-propagation neural network (BP-NN) for MI classification. In this study, EEG data of four subjects provided by BCI competition 2008 was employed. For the comparison of classification accuracy (CA), there were three steps. First, EEG feature extraction for MI was implemented by using a common spatial pattern. Second, SVM and BP-NN were used to classify MI by cross-validation. Finally, the CA rate, receiver operating characteristic curve and area under the curve (AUC) were given to evaluate two classifiers. The average CA rates obtained on the four subjects using SVM and BP-NN were 75.20 and 80.73%, respectively. Furthermore, the mean AUCs of SVM and BP-NN were 0.7860 and 0.9462, respectively. Both average CA rate and AUC indicate that BP-NN has better accuracy of classification than SVM.

Keywords

• signal classification
• neural nets
• electroencephalography
• medical signal processing
• brain-computer interfaces
• backpropagation
• support vector machines
• feature extraction
• svm
• bp-nn
• motor imagery classification
• brain–computer-interface technology
• mi classification
• classification accuracy
• eeg feature extraction
• support vector machine
• backpropagation neural network
• electroencephalogram
• bci technology