About this Research Topic
Brain-Computer Interfaces (BCIs) have been researched now for about 30 years. Still, much of the work done in the lab environment is seldom applied within the specific group of end-users, who are people with severe motor disabilities.
The main goal of the research community should be to finally bring the BCI into a state in which the end-users can profit and gain independence and life quality. One possibility to push the field into practical applications is driven by initiatives like CYBATHLON. This competition, initiated by ETH Zurich, brings together research institutions and industry in several fields to showcase their developments in robotic-assisted aids to end-users; the BCI Race (https://cybathlon.ethz.ch/races-and-disciplines/bci-race.html) is the competition for our field.
In the BCI Race, the end-users are the pilots and they control an avatar in a race against other pilots, by using a multi-class BCI. This competition and others are enormously demanding to the developers, as the BCI system must work properly at the time of the competition, out of the lab in a foreign environment, with spectators around, noise and without second chances.
Another extremely important factor, of course, is the preparation of the team for the competition.
This Research Topic is looking for original scientific papers from researchers and teams who have participated or will participate in online BCI competitions (eg. CYBATHLON BCI Series 2019, CYBATHLON 2020, etc.), with a special focus on the transition from first contact with BCI to the final control of application and the clinical application of BCIs. Of particular interest for this article collection is the training of the end-user (pilot), namely the process of training novice pilots until they are finally able to control a 4-class (multiclass) BCI.
Additional article types such as Reviews or Opinion articles might also be considered.
Areas covered by the Research Topic are:
• User training, especially from the first screening to the online feedback session
• How a multi-class BCI, specifically based on mental tasks, is trained
• Transfer of user data over multiple sessions or adaptive BCIs
• Closed-loop training
Keywords: Brain-Computer Interface (BCI), CYBATHLON BCI Race, electroencephalogram (EEG), closed-loop control, training paradigm, transfer learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.