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 trainertrainee 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.
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 trainertrainee 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.
| Originalsprache | englisch |
|---|---|
| Titel | Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'14) |
| Herausgeber (Verlag) | . |
| Seiten | 1493-1496 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 2014 |
Fields of Expertise
- Human- & Biotechnology