Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns
PLoS ONE. 2015-12-01; 10(12): e0143962
DOI: 10.1371/journal.pone.0143962
Read on PubMed
Jeunet C(1)(2), N’Kaoua B(1), Subramanian S(3), Hachet M(2), Lotte F(2).
Author information:
(1)Laboratoire Handicap & Système Nerveux, University of Bordeaux, Bordeaux,France.
(2)Project-Team Potioc, Inria Bordeaux Sud-Ouest/LaBRI/CNRS, Talence, France.
(3)Interact Lab, University of Sussex, Brighton, United Kingdom.
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.