Rebeca Corralejo

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This study performed an analysis of several feature extraction methods and a genetic algorithm applied to a motor imagery-based Brain Computer Interface (BCI) system. Several features can be extracted from EEG signals to be used for classification in BCIs. However, it is necessary to select a small group of relevant features because the use of irrelevant(More)
The present study aims at developing and assessing an assistive tool for operating electronic devices at home by means of a P300-based brain–computer interface (BCI). Fifteen severely impaired subjects participated in the study. The developed tool allows users to interact with their usual environment fulfilling their main needs. It allows for navigation(More)
Neurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain–computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with an NFT. In our study, we hypothesized that an NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging(More)
The age-related impairment is an increasing problem due to the aging suffered by the population, especially in developed countries. It is usual to use electroencephalogram (EEG)-based Brain Computer Interface (BCI) systems by means of the signal in order to assist and to improve the quality of life of people with disabilities. However, a parallel research(More)
The aim of this study was to develop a Brain Computer Interface (BCI) application to control domotic devices usually present at home. Electroencephalographic (EEG) activity was recorded from users’ scalp and sensorimotor rhythms were used to control the BCI. Our application uses the BCI2000 general purpose system. We studied four feature extraction(More)
Practical motor imagery-based brain computer interface (MI-BCI) applications are limited by the difficult to decode brain signals in a reliable way. In this paper, we propose a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks. Stacked generalization(More)
The difficulty to decode brain signals in a reliable way limits practical motor imagery-based brain computer interface (MI-BCI) applications. The aim of this paper is to propose a classification framework that handle spectral, temporal, and spatial characteristics associated with execution of motor imagery tasks, as well as the temporal variability in EEG(More)
OBJECTIVE Current diagnostic guidelines encourage further research for the development of novel Alzheimer's disease (AD) biomarkers, especially in its prodromal form (i.e. mild cognitive impairment, MCI). Magnetoencephalography (MEG) can provide essential information about AD brain dynamics; however, only a few studies have addressed the characterization of(More)
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