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

My conclusion here is that you did nothing that will help hand recovery. No rehab was given, useless. 

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

• Floriana Pichiorri,
• Jlenia Toppi,
• Valeria de Seta,
• Emma Colamarino,
• Marcella Masciullo,
• Federica Tamburella,
• Matteo Lorusso,
• Febo Cincotti &
• Donatella Mattia 

Journal of NeuroEngineering and Rehabilitation volume 20, Article number: 5 (2023) Cite this article

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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.(So you can measure something, but measurements DO NOTHING FOR RECOVERY!) Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.

Background

Disability after stroke is largely due to residual upper limb motor deficit [1]. This latter is the target of several novel rehabilitation approaches including those based on Brain-Computer Interfaces—BCIs [2, 3]. BCIs for post-stroke motor rehabilitation rely on the principle that reinforcement of close-to-normal motor related brain activity (most frequently derived from electroencephalogram—EEG), results in an improvement of motor function [4]. Hybrid BCIs exploit physiological signals other than brain activity, such as muscular activity derived from surface electromyography (EMG), in order to increase the classification performance [5]. However, such hybrid systems could be employed in the context of post-stroke motor rehabilitation to monitor motor abnormalities, such as spasticity, co-contractions, motor overflow [6,7,8,9,10] in order to reinforce close-to-normal muscular activation.

To this purpose, we recently explored the potential of cortico-muscular coherence (CMC) patterns derived from high-density EEG/EMG as a feature for a rehabilitative hybrid BCI in healthy subjects performing simple hand movements (most commonly employed in BCI paradigms) obtaining high classification performances with the most discriminant EEG–EMG features [11]. With respect to currently available hybrid BCI systems which combine different signals at the classification stage, CMC can be conceived as an intrinsically hybrid feature per se allowing simultaneous monitoring of the interaction between brain (EEG) and muscular (EMG) activity.

Indeed, CMC is a measure of brain-muscle interplay during movement, derived from EEG–EMG coupling within motor relevant EEG frequency bands [12]. CMC is altered after stroke, showing mainly a reduction in EEG–EMG coupling [13]. Until recently, most CMC studies in stroke patients have limited the observation to few EEG electrodes in the affected hemisphere and the target muscle [12,13,14,15]. Similarly, the implementation of CMC-based BCIs has been limited to few EEG–EMG couples determined a priori [16]. However, the complexity of post-stroke recovery is such that several brain regions and muscles participate in post-lesional re-arrangements [17,18,19,20]. Lately, stroke-related CMC studies have broadened the observation to multi-channel recordings to describe complex phenomena such as the contralesional hemisphere contribution [21] or the abnormal recruitment of antagonists and proximal muscles [14, 22, 23]. All this evidence supports the potential role of CMC control feature in a rehabilitative BCI paradigm for its capability to encode both volitional control over movement and possible deviations from the physiological motor system activation, thus well beyond the purpose of increasing system classification performance.

A successful introduction of CMC control feature in rehabilitative BCIs requires to first identify which properties of the widespread corticomuscular network (namely which EEG–EMG features) would best outline the complexity of post-stroke motor deficit to ensure that such hybrid BCI will favor functional motor recovery and eventually discourage maladaptive changes.

In the present study, CMC patterns were estimated by means of high-density recordings to best capture the widespread corticomuscular network properties in stroke patients during the execution of simple hand movements such as grasping and finger extension. With this aim, the network's properties were then characterized by means of ad hoc indices derived from a graph theoretical approach [24]. Statistical analysis was performed to outline differences between healthy subjects and patients, performing the movements both with the affected and unaffected hand (AH, UH), and to seek correlation with upper limb motor impairment as assessed by clinical scales.

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