No abstract

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.

Episodic memory was assessed using Virtual Reality (VR). Forty-four (44) subjects visualized a target virtual apartment containing specific objects in each room. Then they visualized a second virtual apartment comprised of specific objects and objects shared by the two apartments. Subjects navigated in the virtual apartments in one of the following two conditions: active and passive. Four main episodic memory components were scored from the VR exposures: (1) learning effect; (2) active forgetting effect; (3) strategies at encoding and at retrieval; and (4) false recognitions (FRs). The effect of navigation mode (active vs. passive) on each memory component was examined. Active subjects had better learning and retrieval (recognition hits) performances compared to passive subjects. A beneficial effect of active navigation was also observed on the source-based FR rates. Active subjects made fewer source-based FRs compared to passive subjects. These overall results for the effect of active navigation are discussed in terms of the distinction between item-specific and relational processing.

The purpose of this study was to evaluate the effect the visual fidelity of a virtual environment (VE) (undetailed vs. detailed) has on the transfer of spatial knowledge based on the navigation mode (passive vs. active) for three different spatial recall tasks (wayfinding, sketch mapping, and picture sorting). Sixty-four subjects (32 men and 32 women) participated in the experiment. Spatial learning was evaluated by these three tasks in the context of the Bordeaux district. In the wayfinding task, the results indicated that the detailed VE helped subjects to transfer their spatial knowledge from the VE to the real world, irrespective of the navigation mode. In the sketch-mapping task, the detailed VE increased performances compared to the undetailed VE condition, and allowed subjects to benefit from the active navigation. In the sorting task, performances were better in the detailed VE; however, in the undetailed version of the VE, active learning either did not help the subjects or it even deteriorated their performances. These results are discussed in terms of appropriate perceptive-motor and/or spatial representations for each spatial recall task.

No abstract