Using a Noninvasive Decoding Method to Classify Rhythmic Movement Imaginations of the Arm in Two Planes


A brain–computer interface (BCI) will help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a management signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means that of invasive recording techniques, and executed movements are reconstructed using noninvasive decoding techniques. But, it is unclear if detailed imagined movements will be decoded noninvasively using electroencephalography (EEG). We have a tendency to created progress toward imagined movement decoding and successfully classified horizontal and vertical imagined rhythmic movements of the right arm in healthy subjects using EEG. Notably, we tend to used an experimental design which avoided muscle and eye movements to prevent classification results being affected. To classify imagined movements of the identical limb, we decoded the movement trajectories and correlated them with assumed movement trajectories (horizontal and vertical). We then assigned the decoded movements to the assumed movements with the higher correlation. To train the decoder, we have a tendency to applied partial least squares, that allowed us to interpret the classifier weights though channels were highly correlated. To conclude, we tend to showed the classification of imagined movements of 1 limb in two completely different movement planes in seven out of nine subjects. Furthermore, we have a tendency to found a robust involvement of the supplementary motor area. Finally, as our classifier was based on the decoding approach, we tend to indirectly showed the decoding of imagined movements.

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