EEG-based classification of imagined arm trajectories

Abstract

A brain-computer interface (BCI) in combination with a neuroprosthesis can be used to restore movement functionalities in paralyzed persons. The BCI detects the movement imagination (MI) and the neuroprosthesis transforms it into a real movement. Today’s BCIs can detect the process of MI, but not the actual imagined trajectories of, e.g., the arm. Users’ control of a BCI would become more intuitive and natural when the detailed MI, i.e., trajectories, are detected. This is called movement decoding. We made a first attempt to decode MIs, and notably, we did not provoke task depended artefacts like eye movements in our paradigm design. However, that made it necessary to restrict the MIs to movements in two orthogonal planes. We classified the movement plane using a decoding method. For this purpose, we decoded the imagined trajectory and correlated it with two assumed movement trajectories, and then assigned the MI to the assumed movement with the higher correlation. That way, we reached a significant classification accuracy in 7 out of 9 subjects, and showed indirectly the decoding of imagined movements.