Leonardo Duque-Muñoz

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BACKGROUND The extraction of physiological rhythms from electroencephalography (EEG) data and their automated analyses are extensively studied in clinical monitoring, to find traces of interictal/ictal states of epilepsy. METHODS Because brain wave rhythms in normal and interictal/ictal events, differently influence neuronal activity, our proposed(More)
The use of time series decomposition into sub–bands of frequency to accomplish the oscillation modes in nonstationary signals is proposed. Specifically, EEG signals are decomposed into frequency subbands, and the most relevant of them are employed for the detection of epilepsy seizures. Since the computation of oscillation modes is carried out based on(More)
Brain-computer Interfaces (BCIs) are control and communication systems based on acquisition and processing of brain signals to control a computer or an external device. Usually, BCI is focused in recognizing acquired events by different neuroimage methods, but the most used is the electroencephalography (EEG). Feature extraction over EEG signals for BCI(More)
The characterization of daily activities using accelerometers is a currently active research field, with special interest on fall detection of elderly people and sports performance. Most works that account walking and running are peak-acceleration based, however, false positives due to artefact acceleration peaks affect the estimation. Also, the proposed(More)
EEG brain imaging has become a promising approach in Brain-computer interface applications. However, accurate reconstruction of active regions and computational burden are still open issues. In this paper, we propose to use a simplified forward model that includes the reduction of the cortical dipoles based on Brodmann areas together with state-of-the-art(More)