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

Jeunet, Camille, N'Kaoua, Bernard, Subramanian, Sriram, Hachet, Martin and Lotte, Fabien (2015) Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns. PLoS ONE, 10 (12). e0143962. ISSN 1932-6203

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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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QZ Psychology
Depositing User: Camille Jeunet
Date Deposited: 06 Jan 2016 14:01
Last Modified: 02 Jul 2019 20:32

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