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Loic Botrel 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. 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. Those predictors were identified by Hammer et al. In this first study, correlations between relaxation levels or "mean error duration" and accuracy were found but not in both locations. 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. 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. 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. View Show abstract
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