title: Adaptive hybrid brain-computer interaction: ask a trainer for assistance!
abstract: In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.
authors.forename: Gernot R
authors.initials: GR
authors.lastname: Müller-Putz
authors.forename: David
authors.initials: D
authors.lastname: Steyrl
authors.forename: Josef
authors.initials: J
authors.lastname: Faller
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D062207:Brain-Computer Interfaces; D002138:Calibration; D004569:Electroencephalography / Q000379:methods; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D019018:Imagery, Psychotherapy; D007858:Learning; D008297:Male; D009862:Online Systems; D015203:Reproducibility of Results; D055815:Young Adult