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

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

Age-Related Differences and Cognitive Correlates of Self-Reported and Direct Navigation Performance: The Effect of Real and Virtual Test Conditions Manipulation

International audienceThe present study investigated the effect of aging on direct navigation measures and self-reported ones according to the real-virtual test manipulation. Navigation (wayfinding tasks) and spatial memory (paper-pencil tasks) performances, obtained either in real-world or in virtual-laboratory test conditions, were compared between young (n = 32) and older (n = 32) adults who had self-rated their everyday navigation behavior (SBSOD scale). Real age-related differences were observed in navigation tasks as well as in paper-pencil tasks, which investigated spatial learning relative to the distinction between survey-route knowledge. The manipulation of test conditions (real vs. virtual) did not change these age-related differences, which are mostly explained by age-related decline in both spatial abilities and executive functioning (measured with neuropsychological tests). In contrast, elderly adults did not differ from young adults in their self-reporting relative to everyday navigation, suggesting some underestimation of navigation difficulties by elderly adults. Also, spatial abilities in young participants had a mediating effect on the relations between actual and self-reported navigation performance, but not for older participants. So, it is assumed that the older adults carried out the navigation task with fewer available spatial abilities compared to young adults, resulting in inaccurate self-estimates

A Case for Human-Driven Software Development

International audienceHuman-Computer Interaction (HCI) plays a critical role in software systems, especially when targeting vulnerable individuals (e.g., assistive technologies). However, there exists a gap between well-tooled software development methodologies and HCI techniques, which are generally isolated from the development toolchain and require specific expertise. In this paper, we propose a human-driven software development methodology making User Interface (UI) a full-fledged dimension of software design. To make this methodology useful in practice, a UI design language and a user modeling language are integrated into a tool suite that guides the stakeholders during the development process, while ensuring the conformance between the UI design and its implementation

Do experimenters have an influence on MI-BCI user training?

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Towards a Spatial Ability Training to Improve Mental Imagery based Brain-Computer Interface (MI-BCI) Performance: a Pilot Study

International audience—Although Mental Imagery based Brain-Computer Interfaces (MI-BCIs) seem to be very promising for many applications, they are still rarely used outside laboratories. This is partly due to suboptimal training protocols, which provide little help to users learning how to control the system. Indeed, they do not take into account recommendations from instructional design. However, it has been shown that MI-BCI performances are significantly correlated to certain aspects of the users' cognitive profile, such as their Spatial Abilities (SA). Thus, it remains to be elucidated whether training the SA of BCI users would also improve their BCI control performance. Therefore, we proposed and validated an SA training that aimed at being included in an MI-BCI training protocol. Our pre-studies indeed confirmed that such a training does increase people's SA abilities. We then conducted a pilot study with 3 participants, one with a standard MI-BCI training protocol, one with the proposed SA training integrated into a standard MI-BCI training, and another control integrating another training, here verbal comprehension tasks, into a standard MI-BCI training. While such a small population cannot lead to any strong result, our first results show that SA training can indeed be integrated into MI-BCI training and is thus worth being further investigated for BCI user training

Predicting Mental-Imagery Based Brain-Computer Interface Performance from Psychometric Questionnaires

International audienceMental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer via their brain activity, measured while they are performing specific mental tasks. While very promising (e.g., assistive technologies for motor-disabled patients) MI-BCI remain barely used outside laboratories because of the difficulty encountered by users to control such systems. Indeed, although some users obtain very good control performance after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability led the community to look for predictors of MI-BCI control ability. In this paper, we introduce two predictive models of MI-BCI performance, based on a dataset of 17 participants who had to learn to control an MI-BCI by performing 3 MI-tasks: mental rotation, left-hand motor imagery and mental subtraction, across 6 sessions. These models include aspects of participants' personality and cognitive profiles, assessed by questionnaires. Both models, which explain more than 96% and 80% of MI-BCI performance variance, allowed us to define user profiles that could be associated with good BCI performances

Toward distinguishing the different types of attention using EEG signals

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Classification of attention types in EEG signals

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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