Spatial abilities play a major role in BCI performance

Introduction: Despite their promising potential impact for many applications, Mental-Imagery based BCIs (MI-BCIs) remain barely used outside laboratories. One reason is that 15% to 30% of naïve users seem unable to control them [1] and only a few reach high control abilities. Although different predictors of BCI performance (i.e., command classification accuracy) have been investigated to explain this huge inter-user variability [2, 3], no strong predictive model has yet been determined. This could be due to (a) the often small samples used (N=5 or 6) and (b) the fact that these predictors have been mostly determined based on one-session experiments. Yet there is no evidence that performance obtained at the first session is predictive of users' MI-BCI control ability. Material, Methods and Results: In [4], we investigated the impact of the user's personality and cognitive profile on MI-BCI performance based on a 6-session experiment. Averaging performances over these sessions reduced the intra-subject variability (e.g., due to fatigue or external factors), and thus led to a better estimation of participants' MI-BCI control ability. Each session comprised 5 runs during which the participants (N=18) had to learn to perform 3 MI tasks: left-hand motor imagery, mental rotation and mental calculation. The results stressed the impact of mental rotation scores (measured using questionnaires), and which reflect Spatial Abilities (SA), on mean MI-BCI performance [r=0.696, p<0.05] (see Fig. 1[A]). SA are the mental capacities which enable the construction, transformation and interpretation of mental images. In a more recent study (to be published), we trained 20 participants to control a 2-class MI-BCI by performing motor-imagery of their left-and right-hands, within 1 session of 5 runs. Results confirmed the role of SA: mental rotation scores were correlated with peak MI-BCI performance [r=0.464, p<0.05]. This suggests that SA are a generic predictor of MI-BCI performances. Figure 1. [A] Diagram representing the mean classification accuracy for the different subjects as a function of their mental rotation score; [B] One item per exercise included in the Spatial Ability training:the shape on top is the target, and the participant must identify the two shapes that are identical to the target among the four below

A Mendelian polymorphism underlying quantitative variations of goat αs1-casein

Using SDS-polyacrylamide gel electrophoresis and rocket immunoelectrophoresis, 3 new alleles, designated αs1-CnB-,αs1-CnF and αs1-Cno, were identified at the goat αs1-Cn locus, in addition to alleles αs1-CnA, αs1-CnB and αs1-CnC previously reported by BOULANGER al. (1984). Alleles αs1-CnA, αs1-CnB and αs1-CnC are associated with a high content of αs1-casein (approximate mean contribution of each allele being 3.6 g/I) compared to αs1-CnF with a low content (0.6 g/I) and αs1-CnB- with an intermediate content (1.6 g/1) ; αs1-Cno appears to be a true null allele. In a sample of 213 Alpine females from 49 flocks in West Central France, the frequencies of the 6 alleles were : αs1-CnA = 0.14 ; αs1-CnB = 0.05 ; αs1-CnC = 0.01 ; αs1-CnB- = 0.34 ; αs1-CnF = 0.41 ; and αs1-Cno = 0.05. In a sample of 159 Saanen females from 52 flocks of the same region, the frequencies were : αs1-CnA = 0.07 ; αs1-CnB = 0.06 ; αs1-CnC = 0 ; αs1-CnB- = 0.41 ; αs1-CnF = 0.43 ; αs1-Cno = 0.03. Additional data confirm that loci αs1-Cn and αs2-Cn are closely linked. Preliminary investigations indicated a significant superiority in casein content of milks from goats possessing the allele αs1-CnA, as compared to that of milks from goats of genotypes αs1-CnF / αs1-CnF and αs1-CnB- /αs1-CnF and, in a large herd (N = 251), a strong correlation was observed between the αs1-casein content and the rennet-casein content of milk (r = 0.68 ; b = 0.64).A l’aide d’électrophorèses en gel de polyacrylamide SDS et d’immuno-électrophorèses « rocket », 3 allèles, appelés αs1-CnB-, αs1-CnF et αs1-Cno ont été identifiées au locus αs1-Cn de la chèvre, en plus des allèles αs1-CnA, αs1-CnB et αs1-CnC déjà détectés par BOULANGER et al. (1984). Les allèles αs1-CnA, αs1-CnB et αs1-CnC sont associés à un taux élevé de caséine αs1 (contribution approximative de chaque allèle : 3,6 g/I), l’allèle αs1-CnF a un taux faible (0,6 g/I) et l’allèle αs1-CnB a un taux intermédiaire (1,6 g/1). Dans un échantillon de 213 femelles Alpine provenant de 49 troupeaux du centre-ouest de la France, les fréquences des 6 allèles actuellement identifiés étaient les suivantes : αs1-CnA = 0,14 ; αs1-CnB = 0,05 ; αs1-CnC = 0,01; αs1-CnB- = 0,34 ; αs1-CnF = 0,41 et αs1-Cno = 0,05. Dans un échantillon de 159 femelles Saanen provenant de 52 troupeaux de la même région, les fréquences étaient : αs1-CnA = 0,07 ; αs1-CnB = 0,06 ; αs1-CnC = 0; αs1-CnB- = 0,41 ; αs1-CnF = 0,43 ; αs1-Cno = 0,03. Des données supplémentaires confirment que les loci αs1-Cn et αs2-Cn sont étroitement liés. Des investigations préliminaires révèlent que le taux de caséine des laits des chèvres possédant l’allèle αs1-CnA est significativement supérieur à celui des laits des chèvres de génotype αs1-CnF / αs1-CnF ou αs1-CnB- /αs1-CnF; parailleurs, dans un grand troupeau (N = 251), une forte corrélation a été observée entre le taux de caséine αs1 et le taux de matières azotées coagulables (r = 0,68 ; b = 0,64)

In Vivo Retinal Pigment Epithelium Imaging using Transscleral Optical Imaging in Healthy Eyes.

To image healthy retinal pigment epithelial (RPE) cells in vivo using Transscleral OPtical Imaging (TOPI) and to analyze statistics of RPE cell features as a function of age, axial length (AL), and eccentricity. Single-center, exploratory, prospective, and descriptive clinical study. Forty-nine eyes (AL: 24.03 ± 0.93 mm; range: 21.9-26.7 mm) from 29 participants aged 21 to 70 years (37.1 ± 13.3 years; 19 men, 10 women). Retinal images, including fundus photography and spectral-domain OCT, AL, and refractive error measurements were collected at baseline. For each eye, 6 high-resolution RPE images were acquired using TOPI at different locations, one of them being imaged 5 times to evaluate the repeatability of the method. Follow-up ophthalmic examination was repeated 1 to 3 weeks after TOPI to assess safety. Retinal pigment epithelial images were analyzed with a custom automated software to extract cell parameters. Statistical analysis of the selected high-contrast images included calculation of coefficient of variation (CoV) for each feature at each repetition and Spearman and Mann-Whitney tests to investigate the relationship between cell features and eye and subject characteristics. Retinal pigment epithelial cell features: density, area, center-to-center spacing, number of neighbors, circularity, elongation, solidity, and border distance CoV. Macular RPE cell features were extracted from TOPI images at an eccentricity of 1.6° to 16.3° from the fovea. For each feature, the mean CoV was &lt; 4%. Spearman test showed correlation within RPE cell features. In the perifovea, the region in which images were selected for all participants, longer AL significantly correlated with decreased RPE cell density (R Spearman, Rs = -0.746; P &lt; 0.0001) and increased cell area (Rs = 0.668; P &lt; 0.0001), without morphologic changes. Aging was also significantly correlated with decreased RPE density (Rs = -0.391; P = 0.036) and increased cell area (Rs = 0.454; P = 0.013). Lower circular, less symmetric, more elongated, and larger cells were observed in those &gt; 50 years. The TOPI technology imaged RPE cells in vivo with a repeatability of &lt; 4% for the CoV and was used to analyze the influence of physiologic factors on RPE cell morphometry in the perifovea of healthy volunteers. Proprietary or commercial disclosure may be found after the references

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

A user-centred approach to unlock the potential of non-invasive BCIs: an unprecedented international translational effort

Non-invasive Mental Task-based Brain-Computer Interfaces (MT-BCIs) enable their users to interact with the environment through their brain activity alone (measured using electroencephalography for example), by performing mental tasks such as mental calculation or motor imagery. Current developments in technology hint at a wide range of possible applications, both in the clinical and non-clinical domains. MT-BCIs can be used to control (neuro)prostheses or interact with video games, among many other applications. They can also be used to restore cognitive and motor abilities for stroke rehabilitation, or even improve athletic performance.Nonetheless, the expected transfer of MT-BCIs from the lab to the marketplace will be greatly impeded if all resources are allocated to technological aspects alone. We cannot neglect the Human End-User that sits in the centre of the loop. Indeed, self-regulating one’s brain activity through mental tasks to interact is an acquired skill that requires appropriate training. Yet several studies have shown that current training procedures do not enable MT-BCI users to reach adequate levels of performance. Therefore, one significant challenge for the community is that of improving end-user training.To do so, another fundamental challenge must be taken into account: we need to understand the processes that underlie MT-BCI performance and user learning. It is currently estimated that 10 to 30% of people cannot control an MT-BCI. These people are often referred to as “BCI inefficient”. But the concept of “BCI inefficiency” is debated. Does it really exist? Or, are low performances due to insufficient training, training procedures that are unsuited to these users or is the BCI data processing not sensitive enough? The currently available literature does not allow for a definitive answer to these questions as most published studies either include a limited number of participants (i.e., 10 to 20 participants) and/or training sessions (i.e., 1 or 2). We still have very little insight into what the MT-BCI learning curve looks like, and into which factors (including both user-related and machine-related factors) influence this learning curve. Finding answers will require a large number of experiments, involving a large number of participants taking part in multiple training sessions. It is not feasible for one research lab or even a small consortium to undertake such experiments alone. Therefore, an unprecedented coordinated effort from the research community is necessary.We are convinced that combining forces will allow us to characterise in detail MT-BCI user learning, and thereby provide a mandatory step toward transferring BCIs “out of the lab”. This is why we gathered an international, interdisciplinary consortium of BCI researchers from more than 20 different labs across Europe and Japan, including pioneers in the field. This collaboration will enable us to collect considerable amounts of data (at least 100 participants for 20 training sessions each) and establish a large open database. Based on this precious resource, we could then lead sound analyses to answer the previously mentioned questions. Using this data, our consortium could offer solutions on how to improve MT-BCI training procedures using innovative approaches (e.g., personalisation using intelligent tutoring systems) and technologies (e.g., virtual reality). The CHIST-ERA programme represents a unique opportunity to conduct this ambitious project, which will foster innovation in our field and strengthen our community

Heuristic solution methods for the selective disassembly sequencing problem under sequence-dependent costs

The first Waste Framework Directive issued by the European Union dates back to the seventies but was drastically amended in the last decade to reduce environmental impacts of waste by encouraging reuse, recycling and remanufacturing. Product recovery starts with disassembly which results in high labor costs. Disassembly supports environmentally conscious choices like replacement of defective parts to extend the life span of products, removal of suitable components for reuse and extraction of hazardous substances to decontaminate materials for reprocessing. Besides, selective disassembly also accommodates maintenance and repairs. Optimizing the cost of disassembly is crucial to make this process an economically viable option. Due to change tools and parts reorientation, disassembly costs are sequence-dependent. Therefore minimizing the disassembly cost involves the search for an adequate sequence of disassembly tasks. Consequently, this paper addresses the disassembly sequencing problem for selective and sequential disassembly under sequence-dependent costs. As optimal formulations fail to handle real-world cases, we develop a randomized greedy algorithm (needing a very few number of parameters to be set and proving to be robust with respect to their value) and a matheuristic to solve efficiently medium to large-sized instances

Improving the acceptability to enhance the efficiency of stroke rehabilitation procedures based on brain-computer interfaces: General public results

International audienceStroke leaves around 40% of surviving patients dependent in their activities of daily living, notably due to severe motor disabilities [Inserm, 2019]. Brain-Computer Interfaces (BCIs) have been shown to be efficient for improving motor recovery after stroke [Cervera et al., 2018], but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified, they are insufficient: fully optimised BCIs are pointless if patients and clinicians do not want to use them [Blain-Moraes et al., 2012].We hypothesise that improving BCI acceptability and acceptance, by better informing stakeholders about BCI functioning and by personalising the BCI-based rehabilitation procedures to each patient, respectively, will favour engagement in the rehabilitation process and result in an increased efficiency.Our first objective was to identify the factors influencing the intention to use (IU) BCIs [Davis, 1989]. Based on the literature, we constructed a model of BCI acceptability and adapted it in questionnaires addressed to the general population (n=753) and post-stroke patients (n=33). Videos were included, one about the general functioning of BCIs, the second about their relevance for rehabilitation.We used random forest algorithms to explain IU based on our model's factors. After the first video, IU was mainly explained by subjective and personal factors, i.e., perceived usefulness (PU), perceived ease of use (PEOU) and BCI playfulness for the general population, and PU, autonomy and engagement in the rehabilitation for the patients. After the second video, the explanatory factors became more scientific/rational, with PU, cost-benefits ratio and scientific relevance for the general population, and PU, scientific relevance and ease of learning for patients.The shift of main explanatory factors (before/after second video) from subjective representations to scientific arguments highlights the impact of providing patients with clear information regarding BCIs