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Assessing the Benefit of Adaptive Brain-Computer Interfacing
186 Seiten, Dissertation Eberhard-Karls-Universität Tübingen (2013), Softcover, A5
Brain-Computer Interfaces (BCIs) have emerged as a new technology that can either be used to enable communication in people suffering from paralysis or as neurofeedback tool for stroke rehabilitation. Recently, adaptive Brain-Computer Interfaces have been proposed as a new method to improve BCI systems. By continuously adapting to the changing brain signals, the problem of non-stationarity can be alleviated, which leads to more robust BCIs and increased performance.
This thesis tries to assess the benefit of adaptive Brain-Computer Interfacing. While the benefit is tackled from a theoretical side by running simulations of the mutual interaction between an adaptive BCI and a learning user, different adaptive and non-adaptive methods have also been developed to improve BCI performance in online experiments.
To properly determine the benefit of adaptive methods, this thesis does not concentrate on a specific BCI system, but several BCI systems and applications are considered. Different adaptive and non-adaptive methods were developed to improve performance in a MEG-based motor imagery BCI. Furthermore, methods for stroke rehabilitation were proposed and evaluated using non-invasive and invasive methods for recording the brain activity. At last, new methods to improve a BCI based on code-modulated visual evoked potentials were proposed and evaluated. These improvements led to the highest bitrates reported so far for a non-invasive BCI. In addition, it could be shown that adaptive methods can be utilized to allow an unsupervised calibration of a BCI.
Concluding from the results that were obtained for this thesis, adaptive methods do not seem to be overall better suited for improving BCI performance than non-adaptive methods. But adaptive Brain-Computer Interfacing is an important prerequisite that allows to apply BCI technology in new areas of application like stroke rehabilitation or that opens new possibilities like unsupervised calibration of a BCI, which might enable communication with complete locked-in patients.