Single joint movement decoding from EEG in healthy and incomplete spinal cord injured subjects

In this paper, linear regression models will be used to decode individual joint angles from low frequency EEG components. To that end, isotonic flexion/extension knee movements will be analyzed. Particularly, the decoding performance of healthy and incomplete spinal cord injured subjects will be assessed to determine the behavior of this methodology with motor disabled people. When studying cortical activity during walking, the appearance of muscular artifacts severely influences the EEG signals recorded. The analysis of single joint movements should decrease the noise provoked by the gait process itself. Additionally, different time windows prior to the decoded angle will be assessed to obtain a more reliable decoder. The results show that decoding performance is significantly above chance for most of the subjects (both healthy and disabled) and suggests that meaningful information of the movement planning starts around 2.5 seconds prior to the decoded angle.