Classification of real and imaginary hand movements for a BCI design

This paper searches the discrimination ability of a feature extraction method for EEG analysis. The method is tested on the classification of imagined and real right/left hand movements. The study serves for brain computer interface (BCI) applications which help people to control their body via thoughts. According to the results of the three different classifiers which are LDA, SVM and NN, it is concluded that imagination of hand movements can be used instead of real hand movements especially for tetraplegic patients. The classification accuracies of imaginary hand movements of two subjects are 96% and 99% and accuracies of real hand movements are 85% and 77% respectively.