Training Users' Spatial Abilities to Improve Brain-Computer Interface Performance: A Theoretical Approach

National audience—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 ElectroEn-cephaloGraphy-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. Mainly, neurophysiolog-ical and psychological predictors of MI-BCI performance have been proposed. In this paper, a newly-depicted lever to increase MI-BCI performance is introduced: namely a spatial ability training. The aims of this paper are to clarify the relationship between spatial abilities and mental imagery tasks used in MI-BCI paradigms, and to provide suggestions to include a spatial ability training in MI-BCI training protocols

MI-BCI Performances correlate with subject-specific frequency band characteristics

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Using computational modelling to better understand and predict Mental-Imagery based BCI (MI-BCI) users' performance

International audienceMental-Imagery based Brain-Computer Interfaces (MI-BCIs) use signals produced during mental imagery tasks to control the system. Using an MI-BCI requires a dedicated user-training. The more users practice, the better they should become. In other words, their mental commands will most likely be more often correctly recognized by the system. Current MI-BCIs are rather unreliable, which is due at least in part to the use of inappropriate user-training procedures. Understanding the processes underlying user-training by modelling it computationally could enable us to improve MI-BCI training protocols and adapt the latter to the profile of each user. Our objective is to create a statistical/probabilistic model of training that could explain, if not predict, the learning rate and the performances of a BCI user over training time using user's personality, skills, state and timing of the experiment. In order to build such a model, we are currently using data obtained from three different studies [1, 2, 3], which are based on the same protocol. In total, 42 participants were instructed to learn to control an MI-BCI by performing three MI-tasks (i.e., left-hand motor imagery, mental rotation and mental subtraction) across different training sessions (3 to 6 depending on the experiment). Data are divided into four categories: the user's traits (e.g., mental rotation, tension), the user's state (e.g., level of fatigue and difficulty), the timing of the experiment (e.g., hour, lapse between two sessions) and the user performances (e.g., online classification accuracy-CA-, offline cross validation CA). Preliminary analyses revealed positive correlations between MI-BCI performances and mental rotation scores among two of the three studies, suggesting that spatial abilities play a major role in MI-BCI users' abilities to learn to perform MI tasks, which is consistent with the literature [4]

Editorial: Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-Being) Applications

This Research Topic is composed of 11 accepted papers: seven dedicated to original research, a perspective, a mini review and two opinion pieces, and are dedicated to various themes and perspectives. These contributions address the multi-faceted nature of non-clinical BCIs, ranging from ethical ramifications of these neurotechnologies, applications to the arts, education, communication, wellbeing, and sports to the readiness of BCI deployment for gaming

Towards a cognitive model of MI-BCI user training

International audienceMental-Imagery based Brain-Computer Interfaces (MI-BCIs) enable users to control applications using their brain activity alone, by realising mental-imagery tasks. Although promising, MI-BCIs remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. We claim that understanding and improving the user-training process could greatly improve users' MI-BCI control abilities. Yet, to better understand the training process, we need a model of the factors impacting MI-BCI performance. In other words, we need to understand which traits and states impact MI-BCI performance, how these factors interact and how to influence them to improve this performance. Such a model would enable us to design adapted and adaptive training protocols, to guide neurophysiological analyses or design informed classi-fiers, among others. In this paper we propose a theoretical model of MI-BCI tasks, which is the first step towards the design of this full cognitive and computational model

Design and Validation of a Mental and Social Stress Induction Protocol Towards Load-Invariant Physiology-Based Detection

International audienceStress is a major societal issue with negative impacts on health and economy. Physiological computing offers a continuous, direct, and unobtrusive method for stress level assessment and computer-assisted stress management. However, stress is a complex construct and its physiology can vary depending on its source: cognitive workload or social evaluation. To study the feasibility of physiology-based load-invariant psychosocial stress-detection, we designed a stress-induction protocol able to independently vary the relevant types of psychophysiological activity: mental and psychosocial stress. Here, we validate the efficacy of our protocol to induce psychosocial and mental stress. Our participants (N=24) had to perform a cognitive task associated with two workload conditions (low/high mental stress), in two contexts (low/high psychosocial stress), during which we recorded subjects' self-reports, behaviour, physiology and neurophysiology. Questionnaires showed that the subjectively perceived level of stress varied with the psychosocial stress induction, while perceived arousal and mental effort levels vary with mental stress induction. Behaviour and physiology further corroborated the validity of our protocol. Heart rate and skin conductance globally increased after psychosocial stress induction relative to the non-stressful condition. Moreover, we demonstrated that higher workload tasks (mental stress) led to decrease in performance and a marked increase of heart rate

Conception et validation d'un protocole pour induire du stress et le mesurer dans des signaux physiologiques

International audienceLe stress est un problème majeur pour la société de par son impact négatif sur l'économie et la santé. Les nouvelles technologies alliant informatique et physiologie permettent aujourd'hui une mesure du stress non invasive, directe et continue. Cependant, le stress est un procédé complexe dont les corrélats physiologiques peuvent varier selon son origine : psychologique ou psychosociale. Dans ce poster, nous validons la capacité de notre protocole à générer indépendamment du stress psychologique et du stress psychosocial. Nos participants (N=14) devaient réaliser une tâche cognitive associée à deux niveaux de charge mentale (faible/haut stress psychologique) dans deux conditions (faible/haut stress psychosocial) pendant laquelle nous enregistrions des données subjectives (auto-évaluations) et plus objectives (physiologiques et comportementales). Les questionnaires ont montré que le niveau de stress perçu variait avec l'induction de stress psychosocial, alors que l'état d'éveil et l'effort mental perçus variaient avec l'induction de stress psychologique. Les données comportementales et physiologiques ont corroboré la validité de notre protocole. En effet, le rythme cardiaque et la réponse électrodermale ont augmenté après l'induction du stress psychosocial. De plus, on a démontré que les tâches associées à une haute charge cognitive (stress psychologique) induisaient une baisse de la performance et une hausse du rythme cardiaque

Apprentissage humain pour les interfaces cerveau-ordinateur

International audienceNous avons réalisé une revue de la littérature au sujet des protocoles d’entraînement utilisateur actuels pour l’apprentissage des MIBCI. Nous nous sommes plus précisément concentrés sur le protocole proposé par l’équipe BCI de Graz et sur ses variantes. La comparaison de ce protocole avec les recommandations issues de la psychologie et de l’ingénierie pédagogique nous a permis d’établir les limitations des procédures d’entraînement actuelles et de proposer des lignes directrices pour le design de futurs protocols. Notamment, nous insistons sur le fait que l’on devrait fournir aux apprenants des instructions qui spécifient de manière explicite l’objectif de l’entraînement ; que les tâches d’entraînement devraient être adaptives et proposer une augmentation de la difficulté progressive ; que le feedback devrait être multi-modal, explicatif et supportif ; et que l’environnement d’apprentissage devrait être motivant

Towards Explanatory Feedback for User Training in Brain–Computer Interfaces

International audienceDespite their potential for many applications, Brain-Computer Interfaces (BCI) are still rarely used due to their low reliability and long training. These limitations are partly due to inappropriate training protocols, which includes the feedback provided to the user. While feedback should theoretically be explanatory, motivating and meaningful, current BCI feedback is usually boring, corrective only and difficult to understand. In this study, different features of the electroencephalogram signals were explored to be used as a richer, explanatory BCI feedback. First, based on offline mental imagery BCI data, muscular relaxation was notably found to be negatively correlated to BCI performance. Second, this study reports on an online BCI evaluation using muscular relaxation as additional feedback. While this additional feedback did not lead to significant change in BCI performance, this study showed that multiple feedbacks can be used without deteriorating performance and provided interesting insights for explanatory BCI feedback design

Impact of Cognitive And Personality Profiles On Motor-Imagery Based Brain-Computer Interface-Controlling Performance

International audienceBrain-Computer Interfaces (BCIs), and especially those based on Motor-Imagery (MI-BCIs), remain barely used outside laboratories because they are not reliable enough. One reason for users' modest performances at controlling MI-BCIs is the fact that training protocols would not be adapted to them. Thus, in this poster, the impact of users' personality and cognitive profiles on their performances