In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
A Brain-Computer Interface (BCI) application focused on the control of a wheelchair must consider the danger which a wrong command would involve in a real situation. Virtual realit...
—This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI comp...
A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. I...