Privacy provision in eHealth using external services

Privacy provision is a key issue for successful secure access to patients’ health information. Current approaches do not always provide patients with the ability to define suitable rules to access to their information in a secure way. This paper presents an approach to give patients control over their information by means of external services. In this way, health information management and access control are kept independent and more secure.Postprint (published version

Hidden conditional random fields for classification of imaginary motor tasks from EEG data

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem; they do not suffer from some of the limitations of generative models; and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy

Modeling differences in the time-frequency representation of EEG signals through HMM’s for classification of imaginary motor tasks

Brain Computer interfaces are systems that allow the control of external devices using the information extracted from the brain signals. Such systems find applications in rehabilitation, as an alternative communication channel and in multimedia applications for entertainment and gaming. In this work, a new approach based on the Time-Frequency (TF) distribution of the signal power, obtained by autoregressive methods and the use Hidden Markov models (HMM) is developed. This approach take into account the changes of power on different frequency bands with time. For that purpose HMM’s are used to modeling the changes in the power during the execution of two different motor tasks. The use of TF methods involves a problem related to the selection of the frequency bands that can lead to over fitting (due to the course of dimensionality) as well as problems related to the selection of the model parameters. These problems are solved in this work by combining two methods for feature selection: Fisher Score and Sequential Floating Forward Selection. The results are compared to the three top results of the BCI competition IV. It is shown here that the proposed method over perform those other methods in four subjects and the average over all the subjects equals the one obtained by the winner algorithm of the competition

III Congreso Internacional de Historia de America

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN038133 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

«Los bandidos de Río Frío», reconstrucción de un mundo histórico.

Sin resume

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on auto-regressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy