Preliminary study of pedaling motor imagery classification based on EEG signals

This is a preliminary study about which of two classifiers: support vector machine (SVM) or linear discriminant analysis (LDA), and which frequency band: δ (0.1–4Hz), μ (8–12Hz) and β (6–31Hz), provide higher accuracy using brain-computer interface (BCI) for detecting two different cognitive states: pedaling (a motor complex imagery task) and relaxation. Results show that after using independent components analysis, in δ band for 3 out of 5 subjects achieved over 90% of accuracy and the other two over 60% of accuracy.