Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation

Abstract

Background Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks. Methods EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups. Results Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections’ distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients. Conclusions High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.

A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention

“…However, the relationship between brain and muscle activations remains challenging to understand and characterize. Significant advancements have been made in utilizing features that capture this relationship for the control of rehabilitation devices (Chowdhury et al, 2019;Colamarino et al, 2021;de Seta et al, 2022;Guo et al, 2022;Pichiorri et al, 2023). However, they are still Percentage of EEG decoder outputs that were decoded as "Movement" during the trial time for each patient (1-6) and training session, as well as the mean of all of them.…”

“…Hybrid brain-muscle-machine interfaces (hBMI) have been proposed to overcome these limitations. These systems supplement the brain signals used for decoding the patient´s intention by including electromyography (EMG) as a control signal (Leeb et al, 2011;Müller-Putz et al, 2011;Lalitharatne et al, 2013;Kawase et al, 2017;Sarasola-Sanz et al, 2017;Loopez-Larraz et al, 2018;Zhang et al, 2019) or by using features based on the functional connection between the brain and the muscle activity, such as the corticomuscular coherence (Chowdhury et al, 2019;Colamarino et al, 2021;de Seta et al, 2022;Guo et al, 2022;Pichiorri et al, 2023). This, in turn, improves the accuracy of movement intention detection and the feedback given to stroke patients (López-Larraz et al, 2019).…”

Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals

“…Specific inadequacies have been reported in published literature when a single modality is used for bio-signals (Villringer and Chance, 1997 ; Nsugbe et al, 2020 ). A single output from two signals is achieved in a few unique manners and may include the devices' explicit applications and limitations (Park et al, 2023 ; Pichiorri et al, 2023 ). Hybrid methods are implemented to run a simple game control for a healthy person, which may aid in control applications of peripheral devices used by amputees.…”