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International audience; Introduction:While several meta-analyses have demonstrated the relevance of BCIs for improving motor recovery after stroke, these technologies are still barely used in clinical practice. We hypothesise that optimising the acceptability of BCIs could be used as a lever to increase the efficacy of BCI-based motor rehabilitation procedures, and thereby their clinical use. Indeed, a better acceptability will enable a reduction of anxiety levels and an enhancement of motivation and engagement in the procedure. Both will in turn allow for more cognitive resources to be allocated to the task, thus favouring learning and, ultimately, motor recovery. To the best of our knowledge, so far only one study has assessed the relevance of a BCI-based stroke rehabilitation procedure using acceptability measures among their primary criteria of efficacy. Most often, acceptability is only considered as an attribute of user satisfaction, itself being a dimension of user experience. Yet, given its potential impact on BCI use, it seems important to study acceptability as an integral component. Thus, our objective is to rely on validated models depicted in other literature in order to design a first thorough model of acceptability specifically dedicated to BCI-based procedures for motor rehabilitation after stroke. Material, Methods and Results: We designed a model of BCI acceptability based on the technology acceptance model 3 (TAM3) and on the unified theory of acceptance and use of technology 2 (UTAUT2) that assess acceptability in terms of perceived usefulness (PU), perceived ease of use (PEOU) and behavioural intention (BI). Then, we created a questionnaire based on our model in order to estimate its validity and to quantify the influence that each factor had on BCIacceptability. We collected a data sample of 753 respondents representative of the general public in France. We targeted the general public for two main reasons. (i) Due to the high prevalence of stroke, many of us are concerned, more or less directly, by stroke. (ii) Patients’ acceptability is likely to be influenced by the opinions and attitudes of their close relatives (who are part of the general public). Descriptive analyses: Our results suggest that BCIs are associated with high levels of acceptability: PU (8.28/10 ±1.57), PEOU (7.17/10 ±1.57), BI (8.23/10 ±1.69). Validity analyses: Cronbach’s α coefficient analyses revealed a satisfactory internal consistency of our questionnaire, i.e., the items used to measure each factor are mostly consistent and not too redundant (12/17 factors in [0.72;0.95], 4/17 in [0.50;0.62] and 1 = 0.97). Furthermore, a confirmatory factor analysis showed that the structure of the model is adequate. Quantification analyses: Regression analyses based on random forest algorithm revealed that BI is mainly driven by the PU of the system and by the perceived benefits on risk ratio associated with the technology. PU itself is mainly determined by the scientific relevance of BCIs and by PEOU. The main determinants of PEOU are ease of learning and playfulness.Discussion: Our results suggest that beyond the subjective norm (i.e., perceived opinions of our close ones), several factors impact significantly BCI acceptability. Given the weight of the scientific relevance and benefits/risk ratio, it is of utmost importance to clearly inform the population. In addition, the impact of playfulness and ease of learning should encourage us to adapt the rehabilitation procedures to each patient. In order to refine this model, additional data should be collected, in particular with i) patients and caregivers; ii) persons from different cultures; and iii) in different contexts, i.e. for other use cases.

Spastic equinus foot is a common deformity in neurologic patients who compromise walking ability. It is related to the imbalance between weak dorsiflexion and overactive plantar flexor muscles. To achieve the best functional results after surgical management, the challenge is to identify the relevant components involved in the deformity using several methods, namely, examination in the supine position, motor nerve blocks allowing transient anesthesia of suspected overactive muscles, and kinematic and electromyographic data collected during an instrumented 3D gait analysis. The procedure is not standardized; its use varies from one team to another. Access to gait analysis laboratories is limited, and some teams do not perform motor nerve blocks. When both examinations are available, instrumental data from the instrumented 3D gait analysis can be used to specify muscle targets for motor blocks, but data collected from both examinations are sometimes considered redundant.This retrospective cohort analysis compared examination in the supine position, temporary motor nerve blocks, and instrumented 3D gait analysis data in 40 adults after brain or spinal cord injuries. Clinical data collected before motor nerve block was not associated with instrumental data to assess calf muscle's overactivity and tibialis anterior function. Improvement of ankle dorsiflexion in the swing phase after tibial motor nerve block was associated with soleus spastic co-contraction during this phase corroborating its involvement in ankle dorsiflexion defects. This study showed the relevance of tibial motor nerve block to remove spastic calf dystonia and facilitate the assessment of calf contracture. It also underlined the need for complementary and specific analyses of the tibialis anterior abnormal activation pattern after motor nerve block to confirm or deny their pathological nature.

International audience; The adoption of a technology can be defined as the decision made by a person or a society to accept and use it. It is based on two phases during which the willingness to use that technology is assessed. The first phase corresponds to the evaluation of the acceptability of the technology and takes place before the use while the second phase, the technology acceptance, comes afterwards. Assessing the acceptability of BCIs/NF is of utmost importance as it could provide i) insights on the propensity of people to use these technologies and ii) leads to facilitate their adoption.We reviewed the literature dealing with the acceptability of BCIs/NF and using validated questionnaires and models. Through a reproducible search method, we retrieved a list of 55 main references of acceptability questionnaires and models. Then, we searched Scopus, PubMed, and Web of Science to identify the articles in the fields of BCIs/NF that cited these references. We included in our review all the articles reporting experimental results on the acceptability of BCIs/NF that were based on one or several validated acceptability questionnaire(s).Four articles were included in our review. Two of them assessed the acceptability while the others assessed the acceptance of the BCIs. While there are too few articles included to conclude on the acceptability of those technologies, the results are encouraging. BCIs were perceived as useful at 78% by motor impaired individuals who used BCIs for pain management and at 84% by neurotypical individuals who used BCIs to browse an instruction manual. The perceived ease of use varied from 30% to 80%, which could be related to differences in the protocols. Among other factors, the presence of social support appeared to influence the acceptability.

Additional file 2. Additional results file. Figure S4. Learning curves of two example patients’ data (#21 and #29) showing the evolution of a specific parameter over the trials (1 to 10) for the 3 sessions. A. Task time (TaskTime, s). B. Movement time (MovementTime, s). C. Peak velocity (PeakVel, cm/s). D. Hand path ratio (HPR, dimensionless). E. Number of peak velocity (nPeak). F. Game score (Score, %). Figure S5. Evolutions of Minimal Detectable Change (MDC, dashed lines) and Intraclass Correlation Coefficient (ICC, solid lines) according to the number of trials (of sessions 2 and 3), taking into account the calculation of reliability (1: only the 10th trial, to 10: the 10th to the 1st trial). Different parameters are represented by different colors and shapes (black (■): peak velocity, gray (♦): score, orange (●): number of peaks, blue (▲): task time, green (⁃): movement time, yellow (▬): HPR). Figure S6. Learning curves of the averaged time of occurrence of the peak velocity during the movement expressed in percentage (PercPeakVel (%) ±SD), showing the evolution of this parameter over the trials (1 to 10) of the 3 sessions. Between-session significance is represented with asterisks (* p

Additional file 1. Additional methodological file. Additional material S1. Custom Matlab code. The Matlab code used for the calculation of kinematic parameters, available on GitHub website. Figure S2. Box plot comparison between kinematics calculation methods. Comparison between the averaged parameter values provided in the summary report of the ArmeoSpring (Armeocontrol Software) and those calculated with the Matlab code (custom code). Statistical results of the paired t-tests for the hand path ratio (A. HPR), Task Time (B. TaskTime in seconds) and the Score (C. Score in percentage) are reported on the figure. Figure S3. Graphical representation of the average number of failed attempts to catch the consecutive balls (targets 1 to 12). Statistical parameters of the ANOVA are shown below the graph. The number of failed attempts to catch target 1 is significantly lower compared to the other targets (*p

17ème congrès de la SOFAMEA, TOULOUSE, FRANCE, 24-/01/2018 - 26/01/2018; Une orthèse peut être indiquée pour améliorer la marche des patients présentant un défaut de flexion dorsale de cheville en phase oscillante secondaire à une parésie des releveurs de pied. L'objectif de l'étude a été d'évaluer un nouveau modèle hélicoïdal d'orthèse de cheville comparativement à une orthèse en plastique thermoformée et à une condition contrôle sans orthèse.