Classification of Motor Imagery Activities Using Band Power and Second Order Difference Plot

In recent decades, motor imagery (MI) based brain-computer interface (BCI) is acting as a rehabilitation tool for motor disabled people. But it has limited applications due to its lower classification performance. To improve it, this paper introduces band power (BP) and second order difference plot (SODP) for the detection of various motor imagery (MI) activities. First, filter bank technique was implemented to the signals and sets of sub-bands were generated. The BP was evaluated for all sub-bands. To study MI activities more effectively, SODP was applied to each sub-band and area of SODP was calculated. The features (i.e. band power and area of SODP) of all sub-bands were combined and the significant features $(\mathrm{p}\lt 0.05)$ were extracted from one-way analysis of variance (ANOVA). The significant features were fed into multi-class support vector machine (SVM) for the decoding of MI activities. BCI competition 2008 benchmark MI dataset-II-a was used to validate the proposed technique. The performance of the proposed technique was evaluated in terms of classification accuracy (%CA), precision (P), sensitivity (S) and F1-score. The results show that the present technique improved the performance of MI based BCI system and superior to the existing methods reported in the literature.