# Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

Camille O Muller, Germain Faity, Makii Muthalib, S. Perrey, Gérard Dray, Binbin Xu, Jérôme Froger, Denis Mottet, Isabelle Laffont, Marion Delorme, et al.

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Camille O Muller, Germain Faity, Makii Muthalib, S. Perrey, Gérard Dray, et al. Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients. Journal of NeuroEngineering and Rehabilitation, 2024, 21, pp. Article number: 188. 10.1186/s12984-024-01461-3. hal-04342891

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# Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

C. O. Muller<fnref n="1" />,<fnref n="2" />, G. Faity<fnref n="1" />, M. Muthalib<fnref n="1" />,<fnref n="2" />,<fnref n="4" />, S. Perrey<fnref n="1" />, G. Dray<fnref n="1" />, B. Xu<fnref n="1" />, J. Froger<fnref n="1" />,<fnref n="3" />, D. Mottet<fnref n="1" />, I. Laffont<fnref n="1" />,<fnref n="2" />, M. Delorme<fnref n="1" />,<fnref n="3" /> and K. Bakhti<fnref n="1" />,<fnref n="2" />*

**Abstract**

**Background** Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.

**Methods** Twenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.

**Results and conclusions** Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

**Keywords** Neuroplasticity, Sensorimotor cortex, fNIRS, fEEG, Reaching and tracing tasks, Stroke, Upper limb

<footnote>*Correspondence:</footnote>

<footnote>K. Bakhti</footnote>

<footnote>k-bakhti@chu-montpellier.fr</footnote>

<footnote n="1">¹EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France</footnote>

<footnote n="2">²Physical Rehabilitation and Medicine, CHU Montpellier, Montpellier, France</footnote>

<footnote n="3">³Physical Rehabilitation and Medicine, CHU Nîmes, Le Grau du Roi, Nîmes, France</footnote>

<footnote n="4">⁴Silverline Research, Oxford, United Kingdom</footnote>
## Background

Due to its prevalence, functional non-recovery of the paretic upper limb (UL) is a critical concern in stroke rehabilitation [1]. UL functional recovery is mainly attributed to plastic reorganization within the human brain [2, 3], and post-stroke patients often demonstrate abnormal brain activation in comparison to healthy individuals. When using the paretic hand, patients with stroke show increased contralesional and ipsilesional sensorimotor network activation compared to healthy individuals [4], as well as increased activations of contralesional primary motor cortex and bilateral premotor and supplementary motor areas [5]. During the process of functional paretic arm recovery, there is a progressive evolution towards a more “normal” lateralization of the primary sensorimotor cortex [6–10], which underlines the potential of monitoring brain reorganization to predict patients’ responses to rehabilitation [11]. Brain reorganization is classically assessed by functional magnetic resonance imaging (fMRI), mostly in the supine position and during moderately functional tasks such as thumb-finger opposition or elbow flexion-extension [12]. To monitor brain activations under more ecological conditions, i.e., during upright, unrestrained, functional tasks, it is possible to use portable brain imagery techniques such as functional near infrared spectroscopy (fNIRS) and functional electroencephalography (fEEG).

The fNIRS method detects variations in blood-oxygen level-dependant response, as in fMRI [13], and can do so under more ecological conditions [14]. fNIRS measures both oxygenated (HbO₂) and deoxygenated (HbR) hemoglobin in the cerebral cortex blood vessels, and has been previously used to measure sensorimotor network activation during UL movements in healthy young adults [15, 16], older healthy adults [16, 17] and stroke patients [18, 19]. In fully UL functional tasks, such as reaching, studies have identified a bilateral sensorimotor cortex (SM1) activation pattern [16, 20]. Nevertheless, to the best of our knowledge, only one recent study investigated SM1 activation in a stroke population using fNIRS during a reaching task under ecological conditions [18]. They found enhanced ipsi/contralesional SM1 activation in the stroke patients despite poorer motor performance in reaching and grasping.

The fEEG method detects direct variations in electrical currents at the scalp due to local electric fields produced by neuronal activity [21]. Event-related power changes within specific frequency bands (alpha-mu – 8 to 13 Hz and beta – 14 to 29 Hz) reflect the balance between excitation and inhibition in the sensorimotor network [22], classically with an event-related desynchronization (ERD, i.e. power decrease) at movement execution and an event-related synchronization (ERS, i.e. power increase) at rest [23]. In patients with stroke, a number of studies

have shown a relationship between the magnitude of the ERD in the lesioned hemisphere and the paretic UL function [24–26].

Coupling fNIRS and fEEG could provide a better spatio-temporal view of SM1 brain activation patterns in both hemispheres [27]. However, to better understand SM1 activity during fully functional UL tasks, it is important to complement functional brain imaging with kinematic assessments [16]. During forward-reaching tasks, stroke patients often exhibit non-mandatory trunk compensation, i.e. even if they can do with their paretic UL alone, they favour trunk flexion to the detriment of arm use [28, 29]. Unfortunately, this non-use of the paretic UL [30] can lead to maladaptive brain plasticity [31] and hinder functional recovery [32]. Overall, it is now clear that non-mandatory trunk compensation and associated non-use have an impact on the plastic reorganisation of the brain (for a review, see [33]). Thus, investigating how trunk compensation affects SM1 activations during different functional UL tasks (detailed description of UL tasks in Sect. “Experimental design”) may help to understand the mechanisms underlying functional recovery [18].

The primary aim of the present study was to investigate bilateral SM1 activation during functional UL tasks in people with and without stroke. We hypothesised increased SM1 activation in the stroke cohort, both in the ipsilesional and contralesional hemispheres and particularly during performance of the paretic UL. Additionally, we investigated the effect of stroke on the relationship between brain activation patterns and motor performance. Our hypothesis was that individuals in the stroke group would perform worse when using their paretic arm, and that SM1 activation in the injured hemisphere would be positively correlated with task performance.

## Materials and methods

### Participants

The study cohort consisted of 21 post-stroke patients and 21 healthy adults. For the stroke group, the inclusion criteria were to: (i) be aged between 18 and 90 years old, (ii) be at more than 3 months of a first cerebrovascular accident of any aetiology (hemorrhagic or ischemic; participants with several strokes were excluded), and (iii) have an UL motor impairment with FM-UE≥15 [34]. The non-inclusion criteria were to: (i) have hemineglect or severe attentional problems (omission of more than 15 bells on the Bell's test; [35], (ii) have aphasia of comprehension dysfunction (Boston Diagnostic Aphasia Examination<4/5; [36], and (iii) have severe cognitive dysfunction (Mini Mental State Examination-MMSE<24; [37]). To be included, the healthy adults had to be aged between 60 and 90 years old (to fit with the stroke group age) and to be right-handed assessed by the Edinburgh Handedness
Inventory [38]. Exclusion criteria were the existence of neurological (including a history of traumatic brain injury) or motor disorders at the level of the upper limb (history of tendinous disease, arthritis, surgery). Healthy participants were recruited via local association, while stroke ones were recruited at the beginning of a rehabilitation protocol (ReArm project, Clinical trial identifier: NCT04291573, 2nd March 2020).

Table 1 provides detailed participant information, including gender, age, lesioned side, laterality, and clinical scores (refer to the clinical assessments section for additional details). For the stroke group, Table 2 presents all patients' demographic data and clinical history. At the time of the experiment, patients were not included in any intensive acute rehabilitation, and were just following maintenance therapy depending on their needs.

In accordance with the Declaration of Helsinki, this study was approved from the French Research Ethics Committee, (Comité de Protection des Personnes-CPP SUD-EST II, N°ID-RCB: 2019-A00506-51, http://www.cppsudest2.fr/) for the stroke patients, and from the local Ethics Committee of the EuroMov DHM laboratory for the healthy subjects (EuroMov IRB, number 1912B). All participants provided informed written consent prior participation in the study.

## Experimental design

Each participant engaged in an hour-long session in a quiet isolated room. The participants were equipped with the fNIRS-fEEG neuroimaging systems and performed two functional UL tasks while seated: a paced reaching arm task and a circular steering task. The setup permitted synchronized recording of UL kinematics and SM1 activity (fNIRS and fEEG) using lab streaming layer (LSL, https://github.com/labstreaminglayer/App-LabRecorder). More comprehensive details about the functional motor task methodology can be found in our recent methodological paper (see Fig. 5 in [40]).

<table><caption>**Table 1** Characteristics of the participants for each group (n=21)</caption><thead><tr><td>Characteristics</td><td>Healthy group</td><td>Stroke group</td></tr></thead><tbody><tr><td>Age (years) (SD)</td><td>73.1 (±6.7)</td><td>64.4 (±10.2)</td></tr><tr><td>Sex (female/male)</td><td>11/10</td><td>6/15</td></tr><tr><td>Handedness score (SD)</td><td>0.96 (±0.08)</td><td>-</td></tr><tr><td>Paretic arm (right/left)</td><td>-</td><td>8/13</td></tr><tr><td>FM-UE</td><td>-</td><td>48.7 (±5.9)</td></tr><tr><td>WMFT</td><td>-</td><td>57.3 (±9.8)</td></tr><tr><td>BBT ratio</td><td>-</td><td>54.0 (±25.1)</td></tr></tbody></table>

BBT ratio = (paretic score / non-paretic score) * 100. Group comparison showed a significant difference in age (T-test, *p*=.002) and a non-significant difference in sex (Chi-square, *p*=.116)

## Upper-limb function assessments

All participants performed the two functional UL tasks, as detailed in earlier studies [16, 40]. We chose and developed the functional proximal UL tasks that could provide relevant kinematics parameters to understand the movement reorganization (i.e., trunk compensation, movement time, accuracy, speed, performance [28, 29, 41]). There was a gap in the literature at this level, as most of the research projects on task-related brain activity were focused on for distal tasks or tasks that did not use the entire UL (i.e., from trunk to wrist). Since full mobility of the UL is necessary in everyday life activities, it was applicable to use standardized UL movement tasks such as reaching tasks and circular trajectory tracking tasks [16, 42]. Moreover, in the context of stroke, proximal movements, such as arm reaching, can be used to assess patients at the beginning of the recovery process and patient with greater impairment. Indeed, since the process functional recovery has been shown to be proximal-distal direction, patients are most likely to first recover at the level of the proximal UL movements. The reaching task, with maximal condition (maximal arm use with trunk restrained) and spontaneous condition (spontaneous arm use), was previously developed to identify trunk compensation [28], and we further designed the task with a paced rhythm (5 movements per 20s) to particularly enable fNIRS recordings of brain activity changes. The addition of the circular steering task was done in order to allow for a proximal UL performance-based task focused on speed rather than accuracy thus allowing brain activity to be extrapolated to performance.

## Paced reaching task

Participants were seated on a chair fitted with armrests and were instructed to reach a target (a table tennis ball) placed in front of them at a height of 80 cm and a distance which facilitated the complete extension of the arm. A Kinect sensor (V2, Microsoft, USA), sampled at 30 Hz, was positioned 1.70 m above and 1.60 m away from the target. Participants had to reach the target by extending their arm in two conditions: (i) spontaneous condition (i.e., spontaneous arm use, SAU), and (ii) maximal condition (i.e., maximal arm use, MAU), wherein their shoulders were constrained to minimize trunk movements. Each block consisted in five movements per 20-second block, timed to 4s vocal prompts ("go" for 2s; "stop" for 2s) and was interspaced by 20s of rest. After a familiarization block with each arm, participants completed three blocks using their non-dominant/paretic hand, followed by three blocks using their dominant/non-paretic hand in the spontaneous condition. Then, participants repeated the task for three blocks with each hand under the maximal condition.
<table><caption>**Table 2** Demographic information, clinical data and lesion information</caption><thead><tr><th>P</th><th>Age</th><th>Sex</th><th>Hemisphere<br>lesioned</th><th>Months<br>since<br>stroke</th><th>HD before<br>stroke</th><th>Paretic<br>arm</th><th>Lesion<br>localisation</th><th>Vascular territory</th><th>Type of<br>stroke</th><th>Sensitivity<br>deficit</th><th>Spasticity</th><th>MMSE</th><th>FM-UE</th><th>BI</th></tr></thead><tbody><tr><td>1</td><td>62</td><td>M</td><td>L</td><td>8</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>Brainstem</td><td>Is</td><td>yes</td><td>no</td><td>27</td><td>45</td><td>85</td></tr><tr><td>2</td><td>61</td><td>M</td><td>R</td><td>73</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>yes</td><td>no</td><td>30</td><td>55</td><td>95</td></tr><tr><td>3</td><td>52</td><td>M</td><td>R</td><td>84</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>yes</td><td>yes</td><td>24</td><td>51</td><td>90</td></tr><tr><td>4</td><td>63</td><td>M</td><td>L</td><td>88</td><td>L</td><td>R</td><td>R</td><td>cortical</td><td>MCA, ACA</td><td>Is</td><td>yes</td><td>no</td><td>28</td><td>44</td><td>95</td></tr><tr><td>5</td><td>70</td><td>M</td><td>R</td><td>98</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>yes</td><td>no</td><td>29</td><td>51</td><td>100</td></tr><tr><td>6</td><td>73</td><td>F</td><td>R</td><td>223</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>ACoA</td><td>H</td><td>no</td><td>no</td><td>27</td><td>53</td><td>95</td></tr><tr><td>7</td><td>63</td><td>F</td><td>R</td><td>21</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>-</td><td>Is</td><td>no</td><td>yes</td><td>28</td><td>27</td><td>90</td></tr><tr><td>8</td><td>57</td><td>F</td><td>R</td><td>10</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>PO lobe</td><td>H</td><td>no</td><td>no</td><td>25</td><td>60</td><td>90</td></tr><tr><td>9</td><td>74</td><td>M</td><td>R</td><td>49</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>no</td><td>no</td><td>28</td><td>50</td><td>85</td></tr><tr><td>10</td><td>37</td><td>M</td><td>R</td><td>32</td><td>L</td><td>L</td><td>L</td><td>subcortical</td><td>MCA, ACha</td><td>Is</td><td>no</td><td>yes</td><td>29</td><td>46</td><td>95</td></tr><tr><td>11</td><td>68</td><td>M</td><td>R</td><td>43</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>yes</td><td>no</td><td>24</td><td>47</td><td>95</td></tr><tr><td>12</td><td>76</td><td>M</td><td>L</td><td>93</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>None (H stroke)</td><td>H</td><td>yes</td><td>no</td><td>29</td><td>41</td><td>90</td></tr><tr><td>13</td><td>62</td><td>F</td><td>R</td><td>12</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>yes</td><td>no</td><td>28</td><td>45</td><td>85</td></tr><tr><td>14</td><td>49</td><td>F</td><td>R</td><td>25</td><td>R</td><td>L</td><td>L</td><td>cortical</td><td>MCA</td><td>Is</td><td>-</td><td>yes</td><td>27</td><td>54</td><td>95</td></tr><tr><td>15</td><td>82</td><td>M</td><td>L</td><td>45</td><td>R</td><td>R</td><td>R</td><td>-</td><td>-</td><td>Is</td><td>yes</td><td>-</td><td>29</td><td>58</td><td>100</td></tr><tr><td>16</td><td>72</td><td>M</td><td>L</td><td>4</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>ACha</td><td>Is</td><td>no</td><td>no</td><td>29</td><td>44</td><td>90</td></tr><tr><td>17</td><td>66</td><td>M</td><td>L</td><td>9</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>ACha</td><td>Is</td><td>no</td><td>no</td><td>30</td><td>38</td><td>95</td></tr><tr><td>18</td><td>73</td><td>M</td><td>L</td><td>9</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>brainstem</td><td>Is</td><td>no</td><td>yes</td><td>30</td><td>36</td><td>25</td></tr><tr><td>19</td><td>71</td><td>F</td><td>L</td><td>18</td><td>R</td><td>R</td><td>R</td><td>subcortical</td><td>None (H stroke)</td><td>H</td><td>yes</td><td>no</td><td>25</td><td>57</td><td>95</td></tr><tr><td>20</td><td>62</td><td>M</td><td>R</td><td>3</td><td>R</td><td>L</td><td>L</td><td>subcortical</td><td>None (H stroke)</td><td>H</td><td>yes</td><td>no</td><td>29</td><td>46</td><td>90</td></tr><tr><td>21</td><td>60</td><td>M</td><td>R</td><td>9</td><td>R</td><td>L</td><td>L</td><td>subcortical</td><td>MCA</td><td>Is</td><td>no</td><td>no</td><td>27</td><td>43</td><td>90</td></tr></tbody></table>

Abbreviations M, male; F, female; R, right; L, left; HD, hand-dominance; MCA, middle cerebral artery; ACoA, anterior communicating artery; ACA, anterior cerebral artery; ACha, anterior choroidal artery; PO, parieto-occipital; Is, Ischemic; H, Hemorrhagic; MMSE, mini mental state evaluation (score/30); FM, Upper Limb Fugl-Meyer (score/66); BI, Barthel index (score/100). The severity of the motor impairment was evaluating using the FM-UE in accordance with the motor impairment classification in clinical and research settings [39]
## Circular steering task

This task was based on the speed-accuracy trade-off [43]. Participants were seated on a chair in front of a horizontal graphic tablet (A3 size; Wacom, Kazo, Japan) equipped with a stylus affixed to a mouse pad, facing a 24-inch vertical screen projecting a circular target (33-inch circumference) with a 2 cm tunnel. A Kinect was placed above the graphic tablet at the height of 1.70 m. The task was delivered using a lab-made software, the LSL-Mouse (https://github.com/KarimaBak/LSL-Mouse). Participants were instructed to move a cursor as fast as possible in a clockwise direction. During the familiarization phase, participants were instructed to accelerate if movement trajectory errors (any instances outside the 2 cm circular tunnel boundaries) were below 15% (based on pilot testing). The task comprised three blocks for each arm (20s of task with 20s of rest), commencing with their non-dominant/paretic hand.

## Clinical assessments of paretic upper limb impairment

In conjunction with the functional kinematics and brain evaluation, patients' UL motor function was appraised through clinical evaluations. We utilized several recognized and validated tests, including the FM-UE [34, 39], the Box and Block test (BBT [44]), the Wolf-motor function test (WMFT [45]), the Barthel Index (BI [46]), and the Proximal-arm non-use test (PANU [28, 29]). Comprehensive details of these evaluations are described in the cited references.

The FM-UE assesses upper limb motor impairment, while the BBT measures arm and hand grasping function. WMFT evaluates upper limb function, and the BI measures overall functional recovery (independent function in activities of daily living). The PANU test quantifies the amount of shoulder and elbow movements that a post-stroke individual does not use spontaneously, but can use when forced to do so. These tests collectively provide a comprehensive overview of the paretic UL's functional capacity and impairment (for the FM-UE) level in stroke patients.

## Brain activity (fNIRS and fEEG)

Participants wore a custom neoprene head cap equipped with a combined fEEG-fNIRS system to monitor brain activity within the left and right SM1 regions during both functional motor tasks. We utilized a wireless Starstim fNIRS integration system (Starstim8, Neuroelectrics, Barcelona, Spain; Octamon+, Artinis Medical Systems, Elst, The Netherlands) to measure fEEG and fNIRS signals. Details regarding the placement of the 16 channels, comprising 4 fNIRS and 4 fEEG channels per SM1 hemisphere, are outlined in a previous article (see Fig. 1 in [16]).

The 8 fEEG electrodes were positioned in and around the SM1 cortices: C4, FC2, FC6, CP2 in the right hemisphere and C3, FC1, FC3, CP1 in the left hemisphere, in alignment with the international 10–10 system. The electrodes (NG Geltrode, Neuroelectrics, Spain) were filled with electro-gel (Signa Gel®). Using an ear clip, reference electrodes (CMS, DRL) were placed over the right earlobe. The fEEG signals were sampled at a rate of 500 Hz. We controlled the wifi-fEEG device via a software interface (Neuroelectrics Instrument Controller, NIC v 2.0).

For the fNIRS recording, we used a continuous-wave system employing two wavelengths to capture changes in HbO₂ and HbR overlying the left and right SM1, sampling at 10 Hz. The two receivers were positioned at the C1 and C2 locations of the 10–10 fEEG system, with four transmitters placed 3 cm from the receivers using plastic holders. The fNIRS Bluetooth device was managed through a software interface (Oxysoft, v3.2.51.4, Artinis Medical Systems, Elst, The Netherlands).

Following the equipment setup, participants were asked to perform a wrist extension task to verify if the movement induced a hemodynamic response in the SM1.

## Data analysis

### Task performance

The paced reaching and circular steering task kinematics analysis was undertaken based on previous work [28, 29, 47] and LSL-Kinect software (LSL-KinectV2: https://github.com/KarimaBak/LSL-KinectV2). For the paced reaching task, we calculated the proximal-arm non-use (%) and the hand mean velocity (mm/s). For both tasks, we calculated as trunk compensation parameter, the range of trunk anterior flexion (°) representing the use of the trunk to realize the reaching movement. And, we calculated, as arm use parameters the range of elbow extension (°) representing the use of whole arm to perform the movement.

We assessed the speed-accuracy trade-off during the circular steering task using the Index of Performance ($IP_e$ in bits/s [48]). We calculated the Index of Effective Task Difficulty ($ID_e$) with the formula: $ID_e = \frac{2\pi R}{W_e}$, where R represents the subject's mean circular path radius, and $W_e$ denotes the effective path width. We determined $W_e$ using MacKenzie's formula [49]: $W_e = \sqrt{2\pi e * \sigma}$, where $e$ is the standard deviation of the radius. We then computed $IP_e$ by dividing $ID_e$ by the movement time (MT). In addition, we calculated the speed as laps per second and accuracy as bias ($W_e/W$, following [49]) of the movement.

### Brain activity (fNIRS and fEEG)

We processed all fNIRS raw data using the HOMER toolbox in MATLAB (Homer2 NIRS processing package, [50]) with the files generated by the Lab Recorder (xdf files). Pre- and post-processing steps are detailed in
a previous study [16] and a flowchart presenting these
steps is available in the supplementary materials (file 1).
We used the relative changes (Δ) in peak HbO₂ concen-
tration as an indicator of brain activity.

We analysed all fEEG data using the EEGLAB toolbox on MATLAB ([51], version 2021.1), with the files generated by the Lab Recorder (xdf files). Details of pre- and post-processing steps are provided in a previous study [16]. We calculated the event-related spectral perturbations (ERSP) in the alpha (8–13 Hz) and beta (14–29 Hz) rhythms, revealing average power changes in these specific time frequencies. This information provides insight into event-related desynchronization (ERD; power decrease in a specific frequency band relative to baseline, i.e., rest) and synchronization (ERS; power increase in a specific frequency band relative to the task). For fEEG and fNIRS analyses, parameters were averaged by tasks (paced reaching; circular steering), hand condition (dominant / non-paretic; non-dominant / paretic), and hemisphere (contralateral / ipsilesional; ipsilateral / contralesional).

Statistical analyses

Statistical analyses were performed using R software (ver-
sion 4.2.1) and the ggplot2 [52], dplyr [53] and rstatix [54]
packages. Parametric tests were employed following the
validation of data normality via the Shapiro-Wilk test and
visual examination of Q-Q plots. Effects sizes were indi-
cated using the partial eta square (η₂p), with small (0.02),
medium (0.13), and large (0.26) effect sizes noted [55, 56].
A threshold of p < .05 was used for statistical significance.
If necessary, pairwise comparisons were conducted using
t-tests, with the Benjamini-Hochberg procedure applied
for p-value correction in multiple tests [57]. Significant
effects were interpreted only when of sufficient intensity
(η₂p > .02). All values are presented as mean (SD) unless
stated otherwise. In the absence of three-level interaction
effects, only two-level interaction effects were reported
for each factor combination. Note that the degrees of
freedom of the analysis are varied across variables due to
differing exclusion rates for subjects.

Tasks performance and kinematics

The movement parameters for the circular steering
task (IPe, speed, accuracy, range of trunk anterior flex-
ion, range of elbow extension) were evaluated through
a mixed ANOVA, which included group (healthy and
stroke) as a between-subject factor, and hand (non-
paretic/dominant and paretic/non-dominant hand) as
a within-subject factor. Similarly, a mixed ANOVA was
employed for the paced-reaching task (PANU, mean
velocity, range of trunk anterior flexion, range of elbow
extension), incorporating group (healthy and stroke) as a
between-subject factor and hand (non-paretic/dominant

and paretic/non-dominant hand) and condition (spon-
taneous- SAU and maximal- MAU) as within-subject
factors.

Cortical activations

For the analysis of fNIRS peak of ΔHbO₂ and fEEG Alpha and Beta ESRPs, a mixed ANOVA was applied with group (healthy and stroke) as a between-subject factor, and hand (non-paretic / dominant and paretic / non-dominant hand), condition (spontaneous- SAU and maximal- MAU, paced-reaching task), and hemisphere (contralateral / ipsilesional; ipsilateral / contralesional) as within-subject factors.

Brain-movement relationship

In our investigation of the association between perfor-
mance in the circular steering task and brain activation
(fNIRS peak ΔHbO₂) across the groups, we consistently
applied Spearman rank partial correlation analysis. This
approach was chosen to account for the non-normal dis-
tribution of some variables and to maintain consistency
across the analysis, thus enhancing comparability of our
findings. We choose to keep only moderate effects to
avoid false effects, thus, we just present correlation with
at least a r<sub>s2</sub> > 0.25. Only those effects were reported
to facilitate the results presentation. As statistics were
undertaken with a non-parametric Spearman rank corre-
lation, no regression lines were built on the figure repre-
senting the correlations as they would be misleading.

Results

Tasks performance and kinematics

Circular steering task

On the circular steering task (Fig. 1), we found a higher
performance (IPe) in the healthy group and with the dom-
inant hand / non paretic hand for both groups (Group:
F(1,40)=20.52, p=.000, η₂p=.34; Hand: F(1,40)=53.00,
p=.000, η₂p=.57) with no Group x Hand interaction
(F(1,40)=1.97, p=.169, η₂p=.05). For the speed compo-
nent (i.e., time per lap), we found a Group x Hand inter-
action (F(1,40)=5.83, p=.020, η₂p=.13). Post-hoc analysis
showed that the time per lap difference between paretic/
non-dominant and non-paretic/dominant hand, was sig-
nificantly higher for the stroke group, with a longer time
per lap with the paretic arm (Healthy: η₂p=.25; Stroke:
η₂p=.34). Moreover, it shows that the time per lap was
significantly shorter in the healthy group, whatever the
hand. For the accuracy component (i.e., bias), we did
not find any significant effects (healthy / dominant: bias
183 (±56.6); healthy / non-dominant: bias=189 (±49.5);
stroke / non-paretic: bias=202 (±77.9); stroke / paretic:
bias=233 (±96.3).

On the circular steering task, we found that the trunk
compensations were higher in the stroke group when
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Circular steering task performances and strategies (mean ± SD) for the two groups and according to hand trial. (A) Index of performance (IP<sub>e</sub>); (B) Range of trunk anterior flexion; and (C) Time per lap. (* for statistically significant differences at *p* < .05)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Paced reaching task upper limb movement strategies (mean ± SD) for the healthy and stroke groups and according to hand trial and condition (for condition interaction and/or effects). (A) Proximal-arm non-use, PANU; (B) Range of elbow extension; (C) Hand mean velocity; and (D) Range of trunk anterior flexion. (* for statistically significant differences at *p* < .05)</figcaption></figure>

performing with the paretic hand (Group x Hand interaction: $F_{(1,35)}=8.95, p=.005, \eta_{2p}=.20$). For both groups, the range of elbow extension was significantly higher with the dominant / non-paretic hand ($F_{(1,35)}=8.28, p=.007, \eta_{2p}=.19$).

## Paced reaching task

On the paced reaching task (see Fig. 2), we found a Group x Hand interaction on the PANU, range of trunk flexion and hand mean velocity (PANU: $F_{(1,37)}=8.85, p=.005, \eta_{2p}=.19$; range of trunk flexion: $F_{(1,37)}=5.01, p=.031, \eta_{2p}=.12$; hand mean velocity: $F_{(1,37)}=4.93, p=.033$,

$\eta_{2p}=.12$). The range of trunk anterior flexion, and PANU were higher for the stroke paretic hand and at the same time the hand mean velocity was lower. For the range of trunk anterior flexion, we found a Hand x Condition interaction showing that the range of anterior trunk flexion was lower in the maximal condition for the non-dominant / paretic hand ($F_{(1,37)}=4.88, p=.033, \eta_{2p}=.12$). We also found a condition effect on the range of elbow extension, for both groups, it was higher in the maximal condition ($F_{(1,37)}=7.11, p=.011, \eta_{2p}=.16$).
## Brain activity

Brain activity (fNIRS: peak of ΔHbO₂; fEEG: ERD and ERS) during paced reaching and circular steering tasks are presented in Fig. 3 (fNIRS) and Fig. 4 (fEEG) and the statistical results are detailed in the supplementary materials for group, hand, hemisphere, and condition effects and two-way interaction effects with each factor combinations (see Supplementary material files 2 and 3). The significant three-levels interactions are reported in the text.

### fNIRS

**Circular steering** The analysis of the mean ΔHbO₂ peak during the circular steering task showed no significant effects.

**Paced reaching task** The analysis of the mean ΔHbO₂ peak during the paced reaching task showed a higher activation for stroke group with the paretic hand (Group x Hand: F (1,36)=4.51, *p* = .041, η₂p=.11) and a higher activation in the contralateral side compared to the ipsilateral one for both groups (Hemisphere: F (1,36)=6.45, *p* = .016, η₂p=.15). Nevertheless, the three-way interaction Group x Hand x Hemisphere (F (1,36)=2.82, *p*=.102, η₂p=.07) showed a trend for difference between the two hemispheres or the ipsilateral (contralesional) hemisphere being higher than the contralateral (ipsilesional) side for paretic hand use in the stroke group.

### fEEG

**Circular steering task** On the circular steering task (Fig. 4A), we found for Beta ERS a 3-way interaction Group x Hand x Hemisphere (F (1,25)=5.02, *p*=.034, η₂p=.17). Post-hoc comparisons revealed that, for the

stroke group, there was a Hand x Hemisphere interaction (F (1,14)=7.56, *p*=.016, η₂p=.35) showing a lower post-movement synchronization in the contralateral (ipsilesional) hemisphere when performing with the paretic hand (see Fig. 4A). The analysis of the mean ERSP did not show any main or interaction effect of Group on Alpha and Beta ERD nor Alpha ERS.

**Paced reaching task** On the paced reaching task, there was a Group x Brain interaction for the Beta ERD (F (1,23)=4.98, *p*=.036, η₂p=.19). Although there was a tendency to a Group effect (F (1,29)=3.88, *p*=.051, η₂p=.03) showing a smaller Beta desynchronization in the stroke group (Fig. 4B), the post-hoc comparisons between the different modalities of the Group x Brain interaction were too low to emerge, and thus are not shown in Fig. 4B. For the Alpha ERD we did not find any significant main or interaction effect. For the Beta ERS, we found a Hand x Condition interaction showing that for the stroke group, the post-movement Beta synchronization was higher for the maximal condition (F (1,21)=8.80, *p*=.007, η₂p=.30; see Fig. 4C). We also found a Group x Hand x Hemisphere interaction (F (1,21)=5.08, *p*=.035, η₂p=.20). Post-hoc comparison revealed a Hand x Brain interaction for the stroke group (F (1,27)=14.9, *p*=.001, η₂p=.36) showing a higher Beta ERS with the dominant / non-paretic hand in the stroke group. For Alpha ERS, we found a Group x Hemisphere interaction (F (1,16)=4.53, *p*=.049, η₂p=.22). Post-hoc comparisons revealed that there was a Group effect in the ipsilateral hemisphere (F (1,53)=28.8, *p*=.000, η₂p=.35), showing a higher post-movement Alpha synchronization in the ipsilateral hemisphere of the stroke group in comparison to the healthy group (see Fig. 4D). There was also a Hand x Hemisphere interaction (F (1,16)=6.28, *p*=.023,

<figure><img src="image_5.png" /><figcaption>**Fig. 3** Paced reaching task fNIRS mean ΔHbO₂ peak (mean ± SD) for the healthy and stroke groups as a function of hand and hemisphere (ipsilateral in orange; contralateral in cyan). \* For statistically significant differences at *p* < .05: hand effect in the stroke group and hemisphere effect for all groups and conditions</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4** fEEG Beta event-related spectral perturbation (ERSP) (mean ± SD) for the healthy and stroke groups. Circular steering task: lower Beta event-related synchronization (ERS) in contralateral (ipsilesional) hemisphere of the stroke group after paretic arm movement (A). Paced reaching task: Beta event-related desynchronisation (ERD) with a tendency to lower ERD in the stroke group (B), higher Beta ERS in the stroke group for both hemispheres (C), and a higher Alpha ERS for the stroke group in the Ipsilateral hemisphere. (* for statistically significant differences at *p* < .05)</figcaption></figure>

$\eta_{2p} = .28$). Post-hoc comparisons revealed a Hand effect in the contralateral hemisphere (F (1,51)=4.70, *p* = .035, $\eta_{2p} = .08$), with a higher post-movement synchronization in the non-dominant / paretic hand.

## Brain-movement-clinical scores relationship in the stroke group

### Brain-movement relationship

**Circular steering task** The spearman rank correlation analysis for the circular steering task with paretic hand showed that an increased use of the trunk was associated with a higher movement Beta desynchronization on the contralateral (ipsilesional) hemisphere (*p* = .007, r$_{s2}$ = 0.44) and a tendency in the ipsilateral (contralesional) side (*p* = .080, r$_{s2}$ = 0.19, see Fig. 5A). We also found that for a higher IP$_{e}$ and time per lap in the circular steering task, there was a higher post-movement Beta synchronization in the ipsilateral (contralesional) hemisphere and a tendency in the contralateral one (IP$_{e}$ - Ipsilateral: *p* = .000, r$_{s2}$ = 0.69; Contralateral: *p* = .050, r$_{s2}$ = 0.24; Time per lap - Ipsilateral: *p* = .000, r$_{s2}$ = 0.49; Contralateral: *p* = .031, r$_{s2}$ = 0.28, see Fig. 5B).

**Paced reaching task** When reaching in the maximal condition, we found that elbow extension was negatively correlated with the ipsilateral hemisphere (contralesional

peak of $\Delta$HbO$_2$ (*p* = .008; r$_{s2}$ = 0.32, see Fig. 6A) but not for with the contralateral one (*p* = .130; r$_{s2}$ = 0.12). We also found that the slower to do the maximal reaching have a higher post-movement synchronization in the ipsilateral hemisphere (*p* = .003, r$_{s2}$ = 0.47). We found that for a higher spontaneous elbow extension the Beta post-movement synchronisation was higher in both hemispheres (Ipsilateral: *p* = .001, r$_{s2}$ = 0.50; Contralateral: *p* = .000, r$_{s2}$ = 0.62, see Fig. 6B). On the same conditions, the Alpha post-movement synchronization in the ipsilateral hemisphere was also positively correlated to elbow extension (*p* = .009, r$_{s2}$ = 0.37).

### Brain-clinical scores relationship

For the correlation between the brain parameters and the clinical scores, we found that a more marked Alpha ERD on the circular steering task was associated to a lower FM-UE (Non-paretic hand - Ipsilateral: *p* = .000, r$_{s2}$ = 0.62; Non-paretic hand - Contralateral: *p* = .000, r$_{s2}$ = 0.55; Paretic hand - Ipsilateral: *p* = .003, r$_{s2}$ = 0.43; Paretic hand - Contralateral: *p* = .006, r$_{s2}$ = 0.40, see Fig. 7A).

For the maximal condition of the reaching task with the paretic hand, we found a negative correlation between the peak of $\Delta$HbO$_2$ and FM-UE, showing that for a better clinical score there was a lower ipsilateral (contralesional; *p* = .003, r$_{s2}$ = 0.37) and contralateral (ipsilesional: *p* = .033, r$_{s2}$ = 0.23) peak of $\Delta$HbO$_2$ (see Fig. 7B).
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Stroke group correlation between (A) Alpha ERD and the trunk use, and (B) Beta ERS and the index of effective performance (IP<sub>e</sub>) during the circular steering task with the paretic arm</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6** Stroke group correlation between (A) Peak of ΔHbO<sub>2</sub> and elbow extension and (B) Mean Beta ERS and elbow extension during the paced-reaching task with the paretic arm</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7** Stroke group correlation between (A) Alpha ERD during the circular steering task and the FM-UE test and, (B) Peak of ΔHbO<sub>2</sub> during the maximal paced-reaching task and the FM-UE test</figcaption></figure>
Discussion

This study investigated the impact of chronic stroke on
the bilateral SM1 electrical (fEEG) and hemodynamic
(fNIRS) responses during unilateral proximal UL move-
ments. We concurrently recorded bilateral SM1 activity
via combined fNIRS/ fEEG, along with UL movements
using kinematic tracking. Two previously established UL
functional tasks were employed: a paced-reaching task
and a circular steering task designed to interrogate the
speed-accuracy trade-off [16, 28]. Our main finding was
a greater increase in bilateral SM1 activity (fNIRS-peak
ΔHbO₂) for the paretic UL than non-paretic one during
the paced-reaching task. Regarding the movement modi-
fications, we observed that stroke patients showed slower
speeds, increased trunk compensation, and decreased
spontaneous use of the elbow-shoulder joint, particularly
on the paretic side.

For the paced-reaching task, a greater increase in
bilateral SM1 activation was detected with fNIRS dur-
ing the movement with the paretic hand which tended
to coincide with reduced fEEG Beta desynchronization
at the onset of movement. These results might indicate
compensatory brain mechanisms designed to mitigate
the effects of stroke on movement execution. The fEEG
findings presented a lower SM1 excitability in the stroke
group, which was associated to an increased activation
of the fNIRS SM1 when moving the paretic hand. This
aligns with previous studies suggesting reduced brain
asymmetry and increased activity as potential mecha-
nisms of post-stroke motor recovery [58, 59]. These find-
ings can also be compared to the study by [18] which
analysed fNIRS activity during a modified Box & Block
forward reaching test in stroke patients compared to a
healthy cohort. Despite the inferior performance, stroke
patients demonstrated increased lesioned SM1 activity
during paretic arm reaching.

For the circular steering task, our findings indicated
a reduced performance in the stroke group, while task-
related fNIRS peak and fEEG-ERD remained comparable
across both groups. This task relies heavily on visuomotor
control: continuous monitoring and rectification of the
trajectory while moving as fast as possible, embodying
the concept of the continuous speed-accuracy trade-off
[60]. Given these extensive requirements on sensorimo-
tor control networks, this task is a good measure of
neural efficiency, i.e., the amount of neural resources
required to execute a given task [61]. In this context, the
hypothesis of neural efficiency postulates that individu-
als with higher cognitive ability exhibit lower energy con-
sumption in the brain for equivalent tasks [62]. Given the
decreased performance in the stroke cohort and similar
brain activity levels, it could be inferred that these indi-
viduals exhibit reduced neural efficiency when perform-
ing the circular steering task. However, as our study was

confined to the SM1 region, we cannot draw a definitive
conclusion regarding overall neural efficiency. Indeed,
the circular steering task demands a significant level of
visuomotor control, and previous research has suggested
that the prefrontal area plays a substantial role in control-
ling such movements [63]. However, despite the poten-
tial impairment of neural efficiency in SM1, the absence
of significant brain modification in stroke patients may
be explained by considering the circular steering task's
nature. Indeed, the task required maximal performance
from the healthy subjects as well. Moreover, our pre-
vious study found no effects of healthy aging on the
level of fNIRS SM1 activity in this task, as older adults
engaged both SM1 to compensate for their reduced neu-
ral efficiency [16]. We can thus hypothesize that when
performance is maximized ("as fast as possible") for all
participants, brain activity will reach its maximum, and
compensatory mechanisms may rely on alternative neu-
ral pathways, such as the prefrontal areas [63]. Our previ-
ous work also indicated no significant effects of healthy
aging on any brain or kinematics parameters during the
reaching task. This lack of effect underscores the notion
that, the reaching task was, for our healthy adults, con-
siderably simpler and less demanding than the circular
steering task. However, in the present study, we observed
modifications in brain activity during the paced-reaching
task, potentially due to its complexity for post-stroke
patients demonstrating motor compensation to complete
the task successfully as in the circular steering task.

Focusing on the stroke-induced alterations in move-
ment control, we observed the deployment of compen-
satory strategies by stroke patients to accomplish both
functional tasks using their paretic UL. Specifically, in
the circular steering task, stroke patients employed their
trunk to facilitate task completion with their paretic
hand, concomitantly showing reduced use of the elbow-
shoulder joints. Similarly, during the reaching task, we
detected evidence of proximal-arm non-use (i.e., non-
mandatory trunk compensation) when the task was per-
formed with the paretic hand. Additionally, the velocity
of the paretic hand was reduced, a finding of particular
interest given our use of paced reaching, indicating that
the stroke patients were moving slowly to follow the
paced rhythm. This result could be explained by the exis-
tence of strong correlations between clinical scores and
velocity implying that the patients' movement difficul-
ties may be attributed to their level of impairment. This
observation is consistent with prior studies demonstrat-
ing that the speed of the paretic movement is slower than
that of the non-paretic movement [64]. This also aligns
with our findings from the circular steering task, indicat-
ing reduced movement speed in the stroke group, partic-
ularly for the paretic arm.
A secondary aim of this study was to explore the asso-
ciation between brain and movement kinematics. These
results are a first step for a better understanding of the
underlying mechanisms of post-stroke motor recovery
but as exploratory have to be treated with caution. We
identified an association between trunk use and fEEG
Alpha desynchronization in the circular steering task.
This could imply that trunk use necessitates mobilizing
increased neuronal resources across both hemispheres.
Further, we detected alterations in post-movement Beta
synchronization associated with motor performance.
Specifically, a higher IP<sub>e</sub> correlated with increased ERS.
We could hypothesize that, in this task, the high demand
level is sustained by the highest-performing subjects,
who are also likely to move fastest. It is well established
that increased speed correlates with higher neural activ-
ity [65], implying that the ratio between the movement
and rest period could be higher. Regarding the reaching
task, we observed different effects depending on whether
the task was performed spontaneously or maximally. In
the spontaneous reaching task, our findings mirror those
of the circular task, with higher post-movement synchro-
nization observed in better performers. Conversely, the
positive correlation between movement time and Alpha
ERS in the maximal condition is more challenging to
explain. One could hypothesize that the enhanced syn-
chronization for slower performers might be explained
by the extended duration of neural demand they expe-
rience during the task. As they move slower, their SM1
will be engaged for a longer time (i.e., the paced reaching
task typically entails 2s of movement and 2s of rest), lead-
ing to higher synchronization in the ERSP. Nevertheless,
the negative correlation between fNIRS brain activity
and elbow extension could be akin to the circular steer-
ing task, could suggest an over-activation in lower per-
formers who engage their trunk to facilitate movement.
Another hypothesis could be that in the maximal con-
dition, we instruct patients to use their elbow-shoulder
joints maximally. Consequently, those who employ these
joints less frequently will likely require more resources
and increased brain activity. Thus, we could observe
either the effect of trunk use or the effect of effort. How-
ever, our measurements cannot discern which hypothesis
is closer to the truth (i.e., a measure of perceived effort
could have been beneficial).

Lastly, an exploratory aim of this study was to inves-
tigate the association between stroke patients' clini-
cal scores and the corresponding brain parameters. We
observed meaningful correlations that underscore the
potential of fNIRS and fEEG methodologies in the con-
text of stroke rehabilitation [18, 66, 67]. First, we found
that a more pronounced Beta desynchronization at
movement was linked to a lower score on the FM-UE.
It is in line with prior research illustrating that a more

significant event-related desynchronization in the sen-
sorimotor cortex correlates with an enhanced demand
for concentration and excitatory drive of pyramidal cells
during task execution [68]. For example, studies on grip
tasks during rehabilitation have shown that with progres-
sion and motor improvement, there is a reduced require-
ment for cortical engagement and effort to perform the
grip task [69]. Secondly, the inverse correlation between
fNIRS brain activity and FM-UE indicates that a lower
clinical score corresponds with an increased SM1 acti-
vation during the execution of the paced-reaching task.
This is plausible considering the kinematics of the task.
Indeed, we found an association between higher elbow
extension and higher WMFT scores (data not shown).
Which could suggest that patients who utilize their arm
extension more during the reaching task will have higher
clinical scores, and conversely for patients using more
trunk compensation to do the task. It is also known that
elbow extension negatively correlates with trunk com-
pensation [28, 47]. Consequently, patients with greater
upper limb deficits may rely more on their trunk to reach
the target, leading to larger brain activity in response to
the increased demand for the trunk.

The methodology for the seated reaching and circular steering tasks proposed in this paper, including joint kinematics assessment of UL proximal movements and brain SM1 activity, seems well-suited for a pathological population. The combined fEEG and fNIRS methods provide detailed information about the neural and hemodynamic mechanisms underlying movement [70, 71]. Moreover, using these two controlled tasks allows for an ecological evaluation of movement within the context of functional recovery, enabling an assessment as close as possible to daily living activities [29, 43, 47]. And the analysis of movement parameters selected, such as speed, accuracy, and compensation strategies, could indicate the evolution of motor recovery [33]. Moreover, as previous studies suggest using brain laterality as an indicator of motor recovery [72], our evaluation method could be useful in routine assessments to better characterize patients’ conditions. In this study, we identified differences at the level of the kinematics and of the brain suggesting that the method developed was suitable for evaluating the stroke effects. Additionally, by combining these tools, we identified correlations between brain parameters, movement kinematics, and clinical scores. For example, in this paper, we found a brain/movement correlation for trunk use, which is important in post-stroke rehabilitation evaluation [33]. This approach might eventually allow us to identify neural markers of trunk compensation or other movement strategies, though more studies are needed to confirm this. The objective of the present study was to evaluate the effects of stroke on the brain and kinematic strategies using a newly developed method. As such, we
currently lack sufficient information to provide prognostic strategies for motor rehabilitation, indeed knowing the change in the brain and kinematics following rehabilitation is necessary to answer this problem. Nevertheless, we hypothesize that this method will enable a deeper evaluation of the effects of rehabilitation methods used in clinical settings. For example, the currently running ReArm project is using this method to evaluate the effects of transcranial electrical brain stimulation (HD-tDCS) and virtual reality therapy on post-stroke upper-limb motor recovery [40].

This study has several limitations. Firstly, while age and gender matching were not strictly adhered to, the differences observed should not significantly impact the results. Specifically, the gender ratio was more balanced in the healthy group compared to the stroke group (healthy group: 11 women, stroke group: 8 women). However, statistical analysis showed no significant gender difference between the groups (chi-square test, $p=.116$). Therefore, gender is not considered a limitation affecting our findings. Previous literature [73–75] and our prior work [16] further support that sex does not significantly influence the variables we study (fEEG ERD-ERS; fNIRS) in the context of the functional tasks examined. Regarding age, there is a significant difference between the groups (mean age for healthy group=72 years, stroke group=64 years; t-test, $p=.002$). However, the stroke group is younger on average than the healthy group. This is relevant because the effects we are investigating, such as the decline in performance and lateralization, are typically associated with aging. Thus, the younger average age in the stroke group would likely reduce, rather than exaggerate, the differences between the groups. Moreover, correlation analyses showed no significant relationship between age and the studied variables, indicating that the age range is insufficient to show age effects on these variables, supporting our decision not to include age as a covariate in the ANOVA models. Finally, the available literature is indicating that age is not a predictor of the functional recovery [76–78], particularly our previous work did not show any effect of age on most of the brain variables studied here [16]. Additionally, in this study we did not take into account the role of associated cognitive disorders (in particular visuospatial disorders, for example, which certainly interfere a great deal with the circular task) and sensory disorders (also very important for the circular task, which relies heavily on proprioception), as well as spasticity, which interferes a great deal with elbow extension and compensatory movements by the trunk. Lastly, the reaching task, paced at a consistent rhythm for all participants, could present a significant limitation. This speed constraint could lead to an augmented use of compensatory movements in stroke patients to reach the ball at the required speed [79].

## Conclusion

In conclusion, this study provides insight into the impacts of stroke on task-related brain activity and kinematics during unilateral upper limb movements that engage full UL joint movements (i.e., shoulder, elbow, wrist). Our findings highlight the brain and movement compensations associated with a chronic post-stroke population. Additionally, we demonstrate the utility of a combined fNIRS-fEEG recording approach, which correlates with kinematic and clinical scores. The concurrent evaluation of brain and kinematic parameters in ecological settings offers complementary information about the execution of paretic movements, allowing for extracting specific components for targeted intervention during rehabilitation. Moreover, these measures can enrich routine clinical assessments in ecological settings. As perspectives, the ReArm project, of which this study is a part, aims to discern the effects of rehabilitation on these specific brain and kinematic parameters. Furthermore, we aim to investigate their applicability in routine evaluation to facilitate more personalized rehabilitation strategies.

## Abbreviations

<table><tr><td>BBT</td><td>Box and Block Test</td></tr><tr><td>BI</td><td>Barthel Index</td></tr><tr><td>ERD</td><td>Event Related Desynchronization</td></tr><tr><td>ERS</td><td>Event Related Synchronization</td></tr><tr><td>ERSP</td><td>Event Related Spectral Perturbation</td></tr><tr><td>FM-UE</td><td>Fugl-Meyer Upper-Extremity</td></tr><tr><td>IDe</td><td>Index of Task Effective Difficulty</td></tr><tr><td>IPe</td><td>Index of effective performance</td></tr><tr><td>fEEG</td><td>Functional Electroencephalography</td></tr><tr><td>fNIRS</td><td>Functional Near-infrared Spectroscopy</td></tr><tr><td>HbO2</td><td>Oxygenated blood</td></tr><tr><td>HbR</td><td>Deoxygenated blood</td></tr><tr><td>MAU</td><td>Maximal Arm Use</td></tr><tr><td>PANU</td><td>Proximal Arm Non-Use</td></tr><tr><td>SAU</td><td>Spontaneous Arm Use</td></tr><tr><td>SD</td><td>Standard Deviation</td></tr><tr><td>SM1</td><td>Primary Sensorimotor cortex</td></tr><tr><td>WMFT</td><td>Wolf Motor Function Test</td></tr></table>

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12984-024-01461-3.

Supplementary Material 1: Flowchart of the fEEG and fNIRS pre-processing and processing steps. Method based on previous study. (Muller 2023). *Abbreviations* ERSP, event-related spectral perturbation; ERS, event-related synchronization; ERD, event-related desynchronization; RP, relative power; $P_{nv}$, power spectrum; $\Delta HbO_2$, variation of oxygenated blood

Supplementary Material 2: Statistical results of the ANOVA on the fEEG and fNIRS brain parameters for the circular steering task

Supplementary Material 3: Statistical results of the ANOVA on the fEEG and fNIRS brain parameters for the paced-reaching task

## Acknowledgements

We acknowledge Valérie Driss from the Clinical Research and Epidemiologist unit from Montpellier University Hospital for assisting the neuroimaging and kinematics acquisition data.
**Author contributions**

K.B. coordinated the project and obtained the funding. C.O.M. and G.F. wrote the original draft. C.O.M. acquired the data. C.O.M. and G.F. analyzed the data. C.O.M., G.F., M.M., K.B., D.M., I.L. and S.P. participated in the conceptualization of this project and in the interpretation of the data. G.D., B.X., M.M., S.P., K.B. and C.O.M. developed the experimental set-up and analysis solution for the fNIRS-fEEG signals. D.M., M.M., K.B. and G.F. developed the experimental set-up and analysis solution for the kinematics signals. M.M., D.M., S.P., I.L., M.D., J.F., B.X., G.D. and K.B. participated in the reviewing and editing of the manuscript. All authors read and approved the final manuscript.

**Funding**

The clinical trial was funded by the Direction générale de l'offre de soins (DGOS) of the French Ministère des Affaires Sociales et de la Santé (Programme hospitalier de recherche infirmière et paramédicale-PHRIP-2018-0731), with additional support from the LabEx NUMEV (ANR-10-LABX-0020) within the I-Site MUSE (ANR-16-IDEX-0006).

**Data availability**

Data will be available from the corresponding author on reasonable request.

**Declarations**

**Ethics approval and consent to participate**

This study was conducted as part of the ReArm clinical trial (ID-RCB 2019-A00506-51) following ethical approval by the French Committee for the Protection of Individuals (CPP SUD-EST II) for patient inclusion. In addition, the local ethics committee approved the protocol for healthy participants (IRB-EM 1912B). All participants gave written informed consent prior to taking part in the study.

**Consent for publication**

All authors give their consent to publish.

**Competing interests**

The authors declare no competing interests.

Received: 22 November 2023 / Accepted: 6 September 2024

Published online: 22 October 2024

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