Abstract
The use of motion assistance devices improves the rehabilitation process of patients that have motor disabilities. In the case these devices are controlled by brain-machine interfaces, the rehabilitation process can be improved due to neuroplasticity. However, in the case of lower limb rehabilitation, the limited accuracy of the control algorithms is a serious difficulty to overcome. In this research, different EEG signal's processing techniques, based on motor imagery, are tested for a brain-computer interface in an offline scenario, in order to detect the limitations of the models previous to its realtime implementation. The results reveal that motor imagery is very dependent on the subject and that Stockwell Transform provides the best accuracy among the models tested.
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