Manuel Alegre21
César Viteri8
21Manuel Alegre
8César Viteri
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Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of(More)
In Parkinson's disease (PD), the oscillatory activity recorded from the basal ganglia shows dopamine-dependent changes. In the "off" parkinsonian motor state, there is prominent activity in the beta band (12-30 Hz) that is mostly attenuated after dopaminergic therapy ("on" medication state). The on state is also characterized by activity in the gamma (60-80(More)
OBJECTIVE We studied movement-related electroencephalographic oscillatory changes in the alpha and beta range during a sequence of two movements in 7 healthy volunteers, in order to investigate the relationship between these changes and each component in the sequence. METHODS The sequence consisted of a wrist active extension-passive flexion followed by a(More)
Oscillatory activity can be widely recorded in the cortex and basal ganglia. This activity may play a role not only in the physiology of movement, perception and cognition, but also in the pathophysiology of psychiatric and neurological diseases like schizophrenia or Parkinson's disease. Ketamine administration has been shown to cause an increase in gamma(More)
INTRODUCTION Obstructive Sleep Apnea (OSA) is a major risk factor for cardiovascular disease. The goal of this study was to demonstrate whether the use of CPAP produces significant changes in the heart rate or in the heart rate variability of patients with OSA in the first night of treatment and whether gender and obesity play a role in these differences.(More)
Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital(More)
PURPOSE Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal(More)
We studied the effect of stimulus predictability on the alpha and beta changes observed in central regions during stimulus-induced movement paradigms. Six young volunteers were instructed to extend briskly their dominant wrist as soon as possible after hearing a 2000 Hz sound. Two sequences of stimuli were presented to each subject, the first rhythmic at(More)
Independent component analysis (ICA) is a novel technique that can separate statistically independent elements from complex signals. It has demonstrated its utility in separating artifacts and analyzing interictal discharges in EEG. ICA has been used recently in ictal recordings, showing the possibility of isolating the ictal activity. The goal of our study(More)
The recordings of local field potentials in the subthalamic nucleus in patients with Parkinson's disease (PD), carried out through the stimulators implanted to treat the motor symptoms of the disease, show a prominent basal ("off") activity in the beta range, which is attenuated after dopaminergic therapy. A recent study described improvement of(More)