Different methodologies and algorithms are researched in this thesis aiming at overcoming some of the limitations in Brain-computer interfaces (BCIs) in general and for their applications in the context of immersive robotic embodiment systems in particular. Primarily, the challenges of detecting steady-state visual evoked potentials and interaction error-related potentials are investigated. Invariance and variability of the latter are discussed in detail. Further, adaptive and contextual BCIs are introduced as a way out of the bandwidth bottleneck of current BCIs.
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Different methodologies and algorithms are researched in this thesis aiming at overcoming some of the limitations in Brain-computer interfaces (BCIs) in general and for their applications in the context of immersive robotic embodiment systems in particular. Primarily, the challenges of detecting steady-state visual evoked potentials and interaction error-related potentials are investigated. Invariance and variability of the latter are discussed in detail. Further, adaptive and contextual BCIs a...
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