The contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM) classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI desig...

We describe a novel methodology that is applicable in the detection of emotions from speech signals. The methodology is useful if we can safely ignore sequence information since it constructs static feature vectors to represent a sequence of values; this is the case of the current application. In the initial feature extraction part, the speech signals are cut into speech segments of specified d...

We introduce a general framework for string kernels. This framework can produce various types of kernels, including a number of existing kernels, to be used with support vector machines (SVMs). In this framework, we can select the informative subsequences to reduce the dimensionality of the feature space. We can model the mutations in biological sequences. Finally, we combine contributions of s...

We present a machine translation framework based on Kernel Regression techniques. The translation process is modeled as a stringto-string mapping. For doing so, first both source and target strings are mapped to a natural vector space obtaining feature vectors. Afterwards, a translation mapping is defined from the source feature vector to the target feature vector. This translation mapping is l...