RECOMmENceR: RE-establishing COrtico Muscular COMunication to ENhance Recovery. Clinical Validation of BCI-controlled Functional Electrical Stimulation for Upper Limb Rehabilitation After Stroke
RECOMmENceR: RE-establishing COrtico Muscular COMunication to ENhance Recovery. Clinical Validation of BCI-controlled Functional Electrical Stimulation for Upper Limb Rehabilitation After Stroke
The RECOMMENCER project aims at developing and testing a novel hybrid Brain Computer Interface device based on cortico-muscular connectivity, that will be employed to activate Functional Electrical Stimulation (FES) of upper limb muscles. After the technical implementation of the device and its preliminary testing on healthy subject, the investigators will evaluate the effects of a 1 month training with the device (RECOM) on post-stroke patients undergoing standard rehabilitation (add-on). The proposed intervention will be compared with an active physiotherapy training including FES (CTRL) which will be focused on upper limb with similar intensity as the target intervention (also delivered in add-on).
Brain-Computer Interface (BCI) systems for upper limb rehabilitation have proven some efficacy in the context of several randomized controlled trials (RCT). Such systems rely on the possibility reinforce motor-related brain activity (derived eg from electroencephalography, EEG) harnessing neuroplasticity phenomena ultimately leading to favorable motor outcome. Up to now, no hybrid BCI (hBCI) has been tested successfully for this purpose in a clinical trial. Hybrid BCIs include signals from the periphery (most commonly muscles, via surface electromyography, EMG) and are usually employed to improve BCI performances. In a rehabilitative context, the investigators aim to employ EMG signals to characterize common post-stroke abnormalities (spasticity, co-contractions, motor overflow) and possibly include those in a novel hBCI paradigm to promote both volitional control (EEG) over upper limb movement and "close to normal" muscular activation (EMG). To do so, the investigators propose cortico-muscular coherence (CMC) as a hybrid feature for BCI control. CMC is a measure of EEG-EMG synchronization during movement and has been described extensively in post-stroke patients in relation to their residual motor ability. With respect to previous literature, the proposed approach foresees the estimation of CMC as a multimodal integrated EEG-EMG network, comprising multiple EEG electrodes over the scalp bilaterally and several upper limb muscles bilaterally, so as to capture the above mentioned motor abnormalities (eg abnormal recruitment of non-target muscles). The investigators have already tested such feature on healthy subjects and stroke patients offline and verified its capability to detect upper limb movements online (studies published and/or submitted, see references). An already available BCI prototype was adapted with the parameters derived from such offline studies (see references). The RECOM intervention will be validated within a RCT (single blinded, for evaluators) on stroke patients undergoing rehabilitation (add-on). Before and after the intervention, all patients will undergo an extensive clinical evaluation (see outcome measures) and neurophysiological assessment. The latter will consist in a high density EEG-EMG recording during simple/complex upper limb movements. The recording will include a cinematic analysis (inertial measurement units - IMUs). The investigators hypothesize a better functional motor outcome in the affected upper limb for the RECOM group, which will be described by means of common clinical/functional scales combined with EEG/EMG and cinematic evaluation. Furthermore, aspects related to acceptability and usability of the novel system will be evaluated. In detail mood, motivation and satisfaction will be evaluated before (mood motivation) and after (satisfaction) each training session by means of Visual Analogue Scales; workload will be evaluated at the end of second and last training session by means of the Nasa-Task Load Index.
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