Brain-computer interfaces (BCIs) can detect movement imaginations (MI) which can act as a control signal for a neuroprosthesis of a paralyzed person. However, today's non-invasive BCIs can only detect simply qualities of MI, like what body part is subjected to MI. More advanced future non-invasive BCIs should be able to detect many qualities of MI to allow a natural control of a neuroprosthesis. In this preliminary study, we decoded movement targets during a self-paced center-out reaching task, and calculated corresponding spatial patterns in the source space. We were able to decode the movement targets with significant classification accuracy from one out of three subjects during the movement planning phase. This subject showed a distinct spatial pattern over the central motor area.