Rubén Acevedo

Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface

A Brain-Computer Interface (BCI) is a system that provides direct communication between the brain of a person and the outside world. In the present work we use a BCI based on Event Related Potentials (ERP). The aim of this paper is to efficiently solve the classification problem consisting on labeling electroencephalogram records as target (with ERP) or non-target records (without ERP).We evaluate the performance of a BCI by using the Wavelet Packet Transform with the Local Discriminant Basis (LDB) method to find an orthogonal basis that maximizes the difference between the two classes involved. The performance of the LDB patterns and the temporal data (without post-processing) are analyzed with the Fisher Linear Classifier. It is shown that the bets results are obtained with LDB patterns calculated by Daubechies 4 as filter, Sum of Squares as discriminant function and the first 18 more discriminant basis vectors.

Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

Interfaces Cerebro Computadora: Definición, Tipos y Estado Actual

The Brain Computer Interfaces (BCI) are an al- ternative of communication for people with severe motor dis- abilities. A BCI is a system that does not depend on the brains normal output pathways of peripheral nerves and muscles. These systems extract information either from EEG activity recorded from the scalp (non invasive) or the activity of indi- vidual cortical neurons recorded from implanted electrodes (invasive). In this work a synopsis of the state of the art at world-wide and national level is presented, describing the classes of BCI as well as the used paradigms for their imple- mentation.

Filtrado mediante SVD de la onda M del electromiogramade músculos estimulados eléctricamente

The goal of this preliminary study was to investigate the feasibility of using singular value decomposition to eliminate the M-wave from the surface electromyogram (EMG) of an electrically stimulated paretic muscle in order to extract the volitional response. An SVD-based algorithm combining the subspaces method and a subsequent filtering is presented. It was evaluated with EMG signals registered from surface electrically stimulated muscles with simulated paresis and its performance was compared with a conventional fixed comb filter. A power reduction index was calculated. The filtering strategy proposed showed a good performance in static conditions where there were no traces of the M-wave. In dynamic conditions, the SVD-based algorithm was robust but with some remaining M-wave traces. It would be as a consequence of modifications in the data matrix and, therefore, in the subspaces generator columns and the singular values. In general, the fixed filter was very sensitive to input signal disturbances. In all of these conditions there was a greater power reduction for the SVD-based filter than for the fixed filter. The following step would be to evaluate the algorithm with subjects who have muscle paresis and to test it in non-controlled environments.