Publications by authors named "F Hassainia"

Investigations of dream mentation in brain damaged patients have shed some light on the controversial issue of cerebral lateralization of dreaming. To examine further the relationships between brain function and dreaming, we studied REM sleep dream recall and content in four patients having undergone right functional or anatomical hemispherectomy and eight matched control subjects. Patients were found to have the capacity to report dreams to much the same extent as control subjects.

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Statistical probability mapping was used to quantify and localize EEG differences between 27 patients with Alzheimer's disease (AD) and 25 age- and gener-matched controls. Differences in mean activity in four EEG frequency bands (delta, theta, alpha, beta) for wakefulness and for REM sleep were examined, t-statistic maps clearly highlighted common pattern anomalies in AD patients in the two states. More specifically, Alzheimer patients were more affected than control subjects in parieto-temporal and frontal regions.

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A method for producing rapid and accurate scalp electroencephalography, evoked potential and current density mapping is described. This method is based on the spherical spline interpolation, which has been validated as the most suitable function for this application. In comparison with the basic algorithm, the proposed technique is accurate and fast: it reduces the computation time by a factor of 10, while preserving the desired precision level.

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Brain topography mapping is a useful technique for the representation of electrical activity recorded on the scalp. It clarifies spatial and temporal relationships between different cortical areas. In this work we propose a system which includes several enhancements over those previously proposed, such as an optimised interpolation method and a three dimensional reconstruction of maps.

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Significance Probability Mapping (SPM), based on Student's t-statistic, is widely used for comparing mean brain topography maps of two groups. The map resulting from this process represents the distribution of t-values over the entire scalp. However, t-values by themselves cannot reveal whether or not group differences are significant.

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