Post-Stroke Upper-and Lower-Limb Rehabilitation Through Brain-Computer Interface, Robotic Devices and Transcranial Alternating Current & Functional Electrical Stimulations

Abstract

This work presents the application of a rehabilitation protocol using a Brain-Computer Interface (BCI) based on Motor Imagery (MI) and Neurofeedbak (NF) and applying transcranial Alternating Current Stimulation (tACS) and Functional Electrical Stimulation (FES) together with the use of robotic devices like a robotic monocycle and a robotic glove. This protocol uses the concept of Alternating Treatment Design (ATD), in which a single chronic post-stroke patient is submitted to these techniques. The rehabilitation progress was analysed through EEG and clinical metrics, such as Fugl-Meyer Assessment Scale (FMS), Functional Independence Measurement (FIM), Modified Ashworth Scale (MAS), MiniBESTest (MBT), modified Rankin Scale (mRS), Time Up and Go (TUG), 10-Meter Walk Test (10MWT), National Institutes of Health Stroke Scale (NIHSS), Barthel Scale (BI), and surface electromyography (sEMG). Results from these metrics include 6% increase in Fugl-Meyer Assessment Scale (FMS) for upper-limb and 9% increase for lower-limb; 8% increase in Functional Independence Measurement (FIM), and with the an improvement in the FIM score from 5.83 to 6.27; 25% increase in MiniBESTest (MBT); 30% decrease in Time Up and Go (TUG); 18% increase in time and 25% increase in number of steps in 10-Meter Walk Test (10MWT); 25% decrease in NIHSS; and 14% increase in BI. However, there was not variation, comparing the initial and the final evaluation, for the metrics MAS (which maintained the Grade 1) and mRS (maintaining a score of 3). Regarding the results for surface Electromyography (sEMG), there were 5% increase in muscle contraction peak for finger flexors, 2% for tibialis anterior and 21% for rectus femoris. For the EEG analysis, topographic maps show increase of energy in mu and beta rhythms at the end of intervention, and results also indicate that the use of NF enhances MI performance compared to MI alone.