C. Lithari

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.

On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Cholinergic deficiency in Alzheimer's and Parkinson's disease: Evaluation with pupillometry

The aim of the study was to evaluate the cholinergic deficiency in Alzheimers (AD) and Parkinsons disease (PD). For this purpose, pupil size changes and mobility were assessed using a fast-video pupillometer (263 frames/s). Twenty-three (23) patients with probable AD and twenty-two (22) patients with PD (eleven with cognitive impairment and eleven without) entered the study. A full record of the pupils reaction to light was registered. From this data ten (10) parameters were measured and reported. Comparison of those parameters in both group of subjects followed. Patients with probable AD had abnormal pupillary function compared to healthy ageing. All the Pupil Light Reflex (PLR) variables significantly differed between the two groups (p<0.005) except the Baseline Pupil Diameter after 2-min dark adaptation (D1) and the Minimum Pupil Diameter (D2). Maximum Constriction Acceleration (ACmax) was the best predictor in classifying a subject as normal or as an AD with a perfect classification ability (AUC=1, p<0.001). ACmax and Maximum Constriction Velocity (VCmax) were significantly lower in PD patients without and with coexisting cognitive impairment compared to normal subjects (p<0.001). Patients with cognitive impairment had significantly lower levels of ACmax, VCmax and amplitude (AMP=D1-D2) than patients with no cognitive deficits. ACmax and secondarily VCmax were the best predictors in classifying a subject as normal or as a PD patient with or without cognitive impairment. Cognitive and memory impairment, which reflects a cholinergic deficit, may be a crucial pathogenetic factor for the decrease in the aforementioned pupillometric parameters. VCmax and ACmax can be considered as the most sensitive indicators of this cholinergic deficiency.

A framework combining delta event-related oscillations (EROs) and synchronisation effects (ERD/ERS) to study emotional processing

Event-Related Potentials (ERPs) or Event-Related Oscillations (EROs) have been widely used to study emotional processing, mainly on the theta and gamma frequency bands. However, the role of the slow (delta) waves has been largely ignored. The aim of this study is to provide a framework that combines EROs with Event-Related Desynchronization (ERD)/Event-Related Synchronization (ERS), and peak amplitude analysis of delta activity, evoked by the passive viewing of emotionally evocative pictures. Results showed that this kind of approach is sensitive to the effects of gender, valence, and arousal, as well as, the study of interhemispherical disparity, as the two-brain hemispheres interplay roles in the detailed discrimination of gender. Valence effects are recovered in both the central electrodes as well as in the hemisphere interactions. These findings suggest that the temporal patterns of delta activity and the alterations of delta energy may contribute to the study of emotional processing. Finally the results depict the improved sensitivity of the proposed framework in comparison to the traditional ERP techniques, thereby delineating the need for further development of new methodologies to study slow brain frequencies.

The removal of ocular artifacts from EEG signals: A comparison of performances for different methods

The presence of electrooculographic (EOG) artifacts in the electroencephalographic (EEG) signal is a major problem in the study of brain potentials. A variety of algorithms have been proposed to reject these artifacts including methods based on regression and blind source separation (BSS) techniques. None of them has so far been established as the method of choice. In the present study, the performances of five widely used EOG artifact rejection techniques are compared. The compared methodologies include two fully automated regression methods, one based on Least Mean Square (LMS) for its optimization process, and the other on Recursive Least Square (RLS) algorithm, two BSS techniques which use respectively the Extended — Independent Component Analysis (ext — ICA) and the Second Order Blind Identification (SOBI), and finally a time-varying adaptive algorithm based on H ∞ principles (H ∞ — TV). Each algorithm was applied in real EEG data and then their performance quantified in the time domain. The performance of RLS and H ∞ — TV were poor in removing eye — blink artifacts. For the rest of the methods the results supported the use of LMS technique and suggested the need for further research examining the performance of various artifact rejection techniques in both time and frequency domain.

An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence

Emotion identification is beginning to be considered as an essential feature in human-computer interaction. However, most of the studies are mainly focused on facial expression classifications and speech recognition and not much attention has been paid until recently to physiological pattern recognition. In this paper, an integrative approach is proposed to emotional interaction by fusing multi-modal signals. Subjects are exposed to pictures selected from the International Affective Picture System (IAPS). A feature extraction procedure is used to discriminate between four affective states by means of a Mahalanobis distance classifier. The average classifications rate (74.11%) was encouraging. Thus, the induced affective state is mirrored through an avatar by changing its facial characteristics and generating a voice message sympathising with the users mood. It is argued that multi-physiological patterning in combination with anthropomorphic avatars may contribute to the enhancement of affective multi-modal interfaces and the advancement of machine emotional intelligence.

Alcohol Affects the Brain's Resting-State Network in Social Drinkers

Acute alcohol intake is known to enhance inhibition through facilitation of GABAA receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN). To test our hypothesis, electroencephalographic (EEG) measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC) on standardized Low Resolution Electromagnetic Tomography (sLORETA) solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected) in alpha, beta (eyes-open) and theta bands (eyes-closed) following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05). Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo). Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as expected, to increased GABA transmission and functional connectivity, while long-term alcohol consumption may be linked to exactly the opposite effect.

How does the metric choice affect brain functional connectivity networks

Abstract Brain functional connectivity has gained increasing interest over the last few years. The application of Graph Theory on functional connectivity networks (FCNs) has shed light into different topics related to physiology as well as pathology. To this end, different connectivity metrics may be used; however, some concerns are often raised related with inconsistency of results and their associated neurophysiological interpretations depending on the metric used. This paper examines how the use of different connectivity metrics affects the small-world-ness of the FCNs and eventually the neuroscientific evidences and their interpretation; to achieve this, electroencephalography (EEG) data recorded from healthy subjects during an emotional paradigm are utilized. Participants passively viewed emotional stimuli from the international affective picture system (IAPS), categorized in four groups ranging in pleasure (valence) and arousal. Four different pair-wise metrics were used to estimate the connectivity between each pair of EEG channels: the magnitude square coherence (MSC), cross-correlation (CCOR), normalized mutual information (NMI) and normalized joint entropy (NJE). The small-world-ness is found to be varying among the connectivity metrics, while it was also affected by the choice of the threshold level. The use of different connectivity metrics affected the significance of the neurophysiological results. However, the results from different metrics were to the same direction: pleasant images exhibited shorter characteristic path length than unpleasant ones, while high arousing images were related to lower local efficiency as compared to the low arousing ones. Our findings suggest that the choice of different metrics modulates the small-world-ness of the FCNs as well as the neurophysiological results and should be taken into account when studying brain functional connectivity using graph theory.

Towards emotion aware computing: A study of arousal modulation with multichannel event-related potentials, delta oscillatory activity and skin conductivity responses

Emotion identification has recently been considered as a key element in contemporary studies for advanced human-computer interaction. The achievement of this goal is usually attempted via methods incorporating facial expression and speech recognition, as well as, human motion analysis. In this paper it is attempted to fuse multi-modal physiological signals of the autonomic (skin conductance) and central nervous systems (EEG), through the use of appropriate feature extraction procedures discriminating emotional arousal modulations, to a neural network classifier. Thus, skin conductivity responses, evoked-related potential peaks, and delta frequency oscillatory patterns are analyzed for a comparatively large number of subjects exposed to different emotions, evoked by pictures selected from the International Affective Picture System. The achieved neural network classifications were encouraging. It was found that fear was successfully differentiated (100%), pleasant emotions differing in their arousal level were well distinguished (80%), but the discrimination of low arousing negative feelings such as melancholy was more difficult (70%). It is argued that physiological patterning of multimodal recordings may successfully contribute to the enhancement of human computer interaction and emotion aware computing.

Source detection and functional connectivity of the sensorimotor cortex during actual and imaginary limb movement: a preliminary study on the implementation of econnectome in motor imagery protocols

Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome. Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution. Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.