Electroencephalogram

Total sleep deprivation in healthy subjects has a profound effect on the performance on tasks measuring sustained attention or vigilance. We here report how a selective disruption of deep sleep only, that is, selective slow-wave activity (SWA) reduction, affects the performance of healthy well-sleeping subjects on several tasks: a "simple" and a "complex" vigilance task, a declarative learning task, and an implicit learning task despite unchanged duration of sleep. We used automated electroencephalogram (EEG) dependent acoustic feedback aimed at selective interference with-and reduction of-SWA. In a within-subject repeated measures crossover design, performance on the tasks was assessed in 13 elderly adults without sleep complaints after either SWA-reduction or after normal sleep. The number of vigilance lapses increased as a result of SWA reduction, irrespective of the type of vigilance task. Recognition on the declarative memory task was also affected by SWA re...

Diagnosis of autism is one of the difficult problems facing researchers. In this paper, Electroencephalogram (EEG) based Autism diagnosis using Fisher Linear Discriminat (FLD) Analysis is presented. Multivariate analyses of all the channels (via the concatenated signals) were used. Different preprocessing techniques, different ensemble averages, as well as, different feature extraction techniques are studied. The average correct rates are (90%). Raw data features and FFT features are used. Windsor Filtered Data gave the best mean and the lower standard deviation of both raw and FFT features. Over all, FFT features have a better correct rate of 88.14% and lower standard deviation 0.0404 than raw features.

This paper presents an implementation of an ef- fective synchronization between a registration and a stimula- tion module that form part of a steady state visual evoked potential (SSVEP)-based Brain Computer Interface (BCI). Each of the synchronized modules were created and designed in two different platforms as well as in two different worksta- tions, so a particular objective of this work consisted in finding the best platforms to stimulate and register the EEG in order to create a communication channel and protocol between the two different modules. The resulting synchronization was tested with an existing SSVEP stimulation protocol in one subject. Even though the results were promising, a test on a bigger population is needed.

The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics. 0-7803-8740-6/05/$20.00 ©2005 IEEE.

Emerging applications in various fields, such as Ambient Intelligence scenarios or remote biomedical monitoring, currently demand wireless sensor networks with transceivers having extremely low power consumption requirements. This is a key issue in order to decrease battery weight and size and to increase the lifetime of the battery, which usually in these sensing nodes is not replaceable. To achieve these strict power requirements, several solutions have been proposed at various layers. At the physical layer, savings in power consumption are achieved by lowvoltage operation and optimized power-to-performance ratio. Supply voltages of 1V (or less) are anyway mandatory in modern deep submicron technologies to operate reliably due to the extremely thin oxide. Furthermore reduction of the supply voltage (even of not required) strongly reduces power consumption in digital circuits since it scales with supply voltage. Although this is not so simple in analog circuits, they should operate at the same supply voltage than the digital part in mixed-mode systems to avoid the complexity involved in generating various supply voltages.

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on approximately 15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures....

Histamine-releasing neurons are located exclusively in the TM of the hypothalamus, from where they project to practically all brain regions, with ventral areas (hypothalamus, basal forebrain, amygdala) receiving a particularly strong innervation. The intrinsic electrophysiological properties of TM neurons (slow spontaneous firing, broad action potentials, deep afterhyperpolarisations, etc.) are extremely similar to other aminergic neurons. Their firing rate varies across the sleep-wake cycle, being highest during waking and lowest during rapid-eye movement sleep. In contrast to other aminergic neurons somatodendritic autoreceptors (H 3 ) do not activate an inwardly rectifying potassium channel but instead control firing by inhibiting voltage-dependent calcium channels. Histamine release is enhanced under extreme conditions such as dehydration or hypoglycemia or by a variety of stressors. Histamine activates four types of receptors. H 1 receptors are mainly postsynaptically located and are coupled positively to phospholipase C. High densities are found especially in the hypothalamus and other limbic regions. Activation of these receptors causes large depolarisations via blockade of a leak potassium conductance, activation of a non-specific cation channel or activation of a sodium-calcium exchanger. H 2 receptors are also mainly postsynaptically located and are coupled positively to adenylyl cyclase. High densities are found in hippocampus, amygdala and basal ganglia. Activation of these receptors also leads to mainly excitatory effects through blockade of calcium-dependent potassium channels and modulation of the hyperpolarisationactivated cation channel. H 3 receptors are exclusively presynaptically located and are negatively coupled to adenylyl cyclase. High densities are found in the basal ganglia. These receptors mediated presynaptic inhibition of histamine release and the release of other neurotransmitters, most likely via inhibition of presynaptic calcium channels. Finally, histamine modulates the glutamate NMDA receptor via an action at the polyamine binding site. The central histamine system is involved in many central nervous system functions: arousal; anxiety; activation of the sympathetic nervous system; the stress-related release of hormones from the pituitary and of central aminergic neurotransmitters; antinociception; water retention and suppression of eating. A role for the neuronal histamine system as a danger response system is proposed.

Mindfulness-based cognitive therapy (MBCT), a meditation-based maintenance therapy, reduces the relapse risk in individuals suffering from major depressive disorder (MDD). However, only a few studies investigated the psychophysiological mechanisms underlying this protective effect. We examined effects of MBCT on trait rumination and mindfulness, as indicators of global cognitive style, as well as on residual depressive symptoms in a group of recurrently depressed patients (n = 78) in remission. Additionally, alpha asymmetry in resting-state electroencephalogram (EEG) was assessed. Alpha asymmetry has been found to be predictive of affective style and a pattern indicative of stronger relative right-hemispheric anterior cortical activity may represent a trait marker for the vulnerability to develop MDD.