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Structural support vector machine (SVM) is an elegant approach for building complex and accurate models with structured outputs. However, its applicability relies on the availability of efficient inference algorithms—the state-of-the-art training algorithms repeatedly perform inference to compute a subgradient or to find the most violating configuration. In this paper, we propose an exact inference...
Although the field of Brain-Computer Interfacing (BCI) has made incredible advances in the last decade, current BCIs are still scarcely used outside laboratories. One reason is the lack of robustness to noise, artifacts and nonstationarity which are intrinsic parts of the recorded brain signal. Furthermore out-of-lab environments imply the presence of external variables that are largely beyond the...
The computation of task-related spatial filters is a prerequisite for a successful application of motor imagery-based Brain-Computer Interfaces (BCI). However, in the presence of artifacts, e.g., resulting from eye movements or muscular activity, standard methods such as Common Spatial Patterns (CSP) perform poorly. Recently, a divergence-based spatial filter computation framework has been proposed...
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