Chrysoula Kourtidou-Papadeli

Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions.

Men and women seem to process emotions and react to them differently. Yet, few neurophysiological studies have systematically investigated gender differences in emotional processing. Here, we studied gender differences using Event Related Potentials (ERPs) and Skin Conductance Responses (SCR) recorded from participants who passively viewed emotional pictures selected from the International Affective Picture System (IAPS). The arousal and valence dimension of the stimuli were manipulated orthogonally. The peak amplitude and peak latency of ERP components and SCR were analyzed separately, and the scalp topographies of significant ERP differences were documented. Females responded with enhanced negative components (N100 and N200), in comparison to males, especially to the unpleasant visual stimuli, whereas both genders responded faster to high arousing or unpleasant stimuli. Scalp topographies revealed more pronounced gender differences on central and left hemisphere areas. Our results suggest a difference in the way emotional stimuli are processed by genders: unpleasant and high arousing stimuli evoke greater ERP amplitudes in women relatively to men. It also seems that unpleasant or high arousing stimuli are temporally prioritized during visual processing by both genders.

Indicators of sleepiness in an ambulatory EEG study of night driving.

Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individuals level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG datas annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness indicators for future development of driver fatigue countermeasure devices

Affective computing in the era of contemporary neurophysiology and health informatics

Abstract This commentary is a response to Interacting with Computers (Vol 14)—[Interacting Comput. 14 (2002) 119], [Interacting with Comput. 14 (2002) 141], [Interacting Comput. 14 (2002) 93]. Its aim is to discuss the role that neurophysiological measurements, such as EEG and MEG, may play in affective computing. The discussion is drawn upon the light of current experience and practice, as well as, advances envisaged in the fields of health informatics, telecommunications and biomedical engineering. It is explained why HCI research into interface evaluation and affective computing may be greatly enhanced by exploiting the underlying information of neurophysiological recordings.

Effects of mental workload and caffeine on catecholamines and blood pressure compared to performance variations

Caffeine is characterised as a central nervous system stimulant, also affecting metabolic and cardiovascular functions. A number of studies have demonstrated an effect of caffeine on the excretion of catecholamines and their metabolites. Urinary epinephrine and norepinephrine have been shown to increase after caffeine administration. Similar trends were observed in our study in adrenaline (ADR) and noradrenaline (NORADR) levels and additionally a dose dependent effect of caffeine. The effect of caffeine on cognitive performance, blood pressure, and catecholamines was tested under resting conditions and under mental workload. Each subject performed the test after oral administration of 1 cup and then 3 cups of coffee. Root mean square error (RMSE) for the tracking task was continuously monitored. Blood pressure was also recorded before and after each stage of the experiment. Catecholamines were collected and measured for three different conditions as: at rest, after mental stress alone, after one dose of caffeine under stress, and after triple dose of caffeine under stress. Comparison of the performance of each stage with the resting conditions revealed statistically significant differences between group of smokers/coffee drinkers compared with the other two groups of non-coffee drinkers/non-smokers and non-smokers/coffee drinkers. There was no statistically significant difference between the last two groups. There was an increase of urine adrenaline with 1 cup of coffee and statistically significant increase of urine noradrenaline. Both catecholamines were significantly increased with triple dose of caffeine. Mental workload increased catecholamines. There was a dose dependent effect of caffeine on catecholamines.

Maximum cognitive performance and physiological time trend measurements after caffeine intake

The effect of caffeine on the central nervous system and cardiorespiratory system was tested under resting conditions and while undertaking a multitask performance test. The subjects abstained from caffeine for a week before the study. Each subject performed the test after oral administration of 90 and 250 mg of caffeine on two separate days. Sum of Squares was recorded during the whole period of the study. Heart rate (HR) and respiration rates (RR) were continuously recorded and blood pressure (BP) was recorded before and after each stage of the experiment (Test(0), Test(1), Test(2), Test(3)). Sixteen healthy volunteers participated in the study divided into three groups: Group A, non-smokers and non-coffee drinkers; Group B, smokers and coffee drinkers; and Group C, non-smokers and coffee drinkers. Comparison of the performance of each stage with the resting conditions revealed statistically significant differences of group B compared to the other two groups and no significant differences between Groups A and C in both doses of caffeine. Non-coffee drinkers needed a low dose of caffeine for their optimal performance while a higher dose significantly increased their blood pressure. Coffee drinkers and smokers needed a higher dose of caffeine for optimal performance, which increased very quickly, but did not last and increased their BP. This increase in BP was not statistically significant, probably because of nicotines effect. Heart rate was decreased and respiration rate increased significantly. The optimal performance was dose-dependent, increasing significantly with the higher dose of caffeine but with adverse effects on BP and RR.

From Precambrian Iron-Formation to Terraforming Mars: The JIMES Expedition to Santorini

ABSTRACT The iron embayments at Santorini, Greece, have long been considered by geologists to be the most useful modern environment for understanding variables related to precipitation of Precambrian iron-formation. To help understand the rock record, the embayments were studied almost monthly for a year to assess seasonal variations in iron bacteria and diatoms along with mineralogy, weather, water chemistry, and ecology. Unidentified red rods dominated and accounted for most ferrihydrite production. Diatom abundance was seasonal, including Parlibellus delognei which produces molecular oxygen within iron-coated sheaths. The gross structures of the microbial iron precipitates were in the form of rods, spheres, and braids. Speculations resulting from our observations suggest that lifes origin could have been intimately related to chemical/physical processes occurring where volcanic sources discharged iron through highly porous siliceous substrates and into the primitive ocean. The diverse community also provides a potentially useful ecosystem for Mars terraforming experiments.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Exploring the Neuroplastic Effects of Biofeedback Training on Smokers

Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.

Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings

This paper focuses on developing a novel approach to automatic sleep stage classification based on electroencephalographic (EEG) data. The proposed methodology employs contemporary mathematical tools such as the synchronization likelihood and graph theory metrics applied on sleep EEG data. The derived features are then fitted into three different machine learning techniques, namely k-nearest neighbors, support vector machines and neural networks. The evaluation of their comparative performance is investigated according to their accuracy. Interestingly, the support vector machine achieves the maximum possible accuracy, i.e., 89.07%, which renders it as a suitable method for sleep stage classification.