@phdthesis{Botrel2018,
  author    = {Botrel, Loic},
  title     = {Brain-computer interfaces (BCIs) based on sensorimotor rhythms - Evaluating practical interventions to improve their performance and reduce BCI inefficiency},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-168110},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {Brain computer interfaces based on sensorimotor rhythms modulation (SMR-BCIs) allow people to emit commands to an interface by imagining right hand, left hand or feet movements. The neurophysiological activation associated with those specific mental imageries can be measured by electroencephalography and detected by machine learning algorithms. Improvements for SMR-BCI accuracy in the last 30 years seem to have reached a limit. The currrent main issue with SMR-BCIs is that between 15\% to 30\% cannot use the BCI, called the "BCI inefficiency" issue. Alternatively to hardware and software improvements, investigating the individual characteristics of the BCI users has became an interesting approach to overcome BCI inefficiency. In this dissertation, I reviewed existing literature concerning the individual sources of variation in SMR-BCI accuracy and identified generic individual characteristics. In the empirical investigation, attention and motor dexterity predictors for SMR-BCI performance were implemented into a trainings that would manipulate those predictors and lead to higher SMR-BCI accuracy. Those predictors were identified by Hammer et al. (2012) as the ability to concentrate (associated with relaxation levels) and "mean error duration" in a two-hand visuo-motor coordination task (VMC). Prior to a SMR-BCI session, a total of n=154 participants in two locations took part of 23 min sessions of either Jacobson's Progressive Muscle Relaxation session (PMR), a VMC session, or a control group (CG). No effect of PMR or VMC manipulation was found, but the manipulation checks did not consistently confirm whether PMR had an effect of relaxation levels and VMC on "mean error duration". In this first study, correlations between relaxation levels or "mean error duration" and accuracy were found but not in both locations. A second study, involving n=39 participants intensified the training in four sessions on four consecutive days or either PMR, VMC or CG. The effect or manipulation was assessed for in terms of a causal relationship by using a PRE-POST study design. The manipulation checks of this second study validated the positive effect of training on both relaxation and "mean error duration". But the manipulation did not yield a specific effect on BCI accuracy. The predictors were not found again, displaying the instability of relaxation levels and "mean error duration" in being associated with BCI performance. An effect of time on BCI accuracy was found, and a correlation between State Mindfulness Scale and accuracy were reported. Results indicated that a short training of PMR or VMC were insufficient in increasing SMR-BCI accuracy. This study contrasted with studies succeeding in increasing SMR-BCI accuracy Tan et al. (2009, 2014), by the shortness of its training and the relaxation training that did not include mindfulness. It also contrasted by its manipulation checks and its comprehensive experimental approach that attempted to replicate existing predictors or correlates for SMR-BCI accuracy. The prediction of BCI accuracy by individual characteristics is receiving increased attention, but requires replication studies and a comprehensive approach, to contribute to the growing base of evidence of predictors for SMR-BCI accuracy. While short PMR and VMC trainings could not yield an effect on BCI performance, mindfulness meditation training might be beneficial for SMR-BCI accuracy. Moreover, it could be implemented for people in the locked-in-syndrome, allowing to reach the end-users that are the most in need for improvements in BCI performance.},
  subject      = {Gehirn-Computer-Schnittstelle},
  language  = {en}
}