Abstract
To improve upper limb neurorehabilitation in chronic stroke patients, we apply new methods and tools of clinical training and machine learning for the design and development of an intelligent system allowing the users to go through the process of self-controlled training of impaired motor pathways. We combine the brain-computer interface (BCI) technology with a robotic splint into a compact system that can be used as a robot-assisted neurorehabilitation tool.
First, we use the mirror therapy (MT) which represents a mental process where an individual rehearses a specific limb movement by reflecting the movements of the non-paretic side in the mirror as if it were the affected side. This step is not used for improving the motor functions only, but also for identification of subject's specific electroencephalogram (EEG) oscillatory elemental patterns or ''atoms'' associated with imagery or real hand movements. We estimate these EEG atoms using a multiway analysis, specifically the parallel factor analysis (PARAFAC) for modeling. Using the data from a longitudinal case study, we will report statically significant effects of the MT on the modulation of sensorimotor EEG atoms of a patient with chronic upper limb impairment due to a stroke.
Second, we introduce the BCI-based robotic system operating on the principle of the motor imagery and incorporating a reward-based physical movement of the impaired upper limb. The novelty of this approach lies in the design of the control protocol which uses spatial and frequency weights of the previously estimated sensorimotor atoms during the MT sessions. By projecting the recorded EEG onto the spatial and frequency weights, one-dimensional time scores of the atoms are computed. Getting under the empirically preset threshold of the scores triggers the robotic splint which executes the physical movement with the impaired hand (up and down). We will report analytical and clinical results of three patients with different severity of the upper limb impairment due to a stroke and different lengths of the proposed neurorehabilitation training.
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