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Evaluating decoding performance of upper limb imagined trajectories during center-out reaching tasks

In recent years, several studies have shown that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters after applying linear decoders. These studies have been generally conducted using actual upper limb movements, but so far there has been little discussion about the possibility of applying these decoders to motor imagery tasks. Moreover, the use of these decoders is rather controversial and there is no general agreement about the metrics used to compare decoded and real kinematics. In this paper, we have applied this methodology to upper limb imagined movements using a center-out protocol. Our results show that, although decoding performance is poor, there are significant components, particularly in horizontal imagined movements, that could be translated into reliable output commands. For this purpose, we have proposed a discrete classification of reached targets showing significant classification rates when the number of classified targets decreases.