Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented.