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Highlights Behavioral and EEG effects of multifocal frontoparietal tDCS are investigated in patients with severe brain injury. No behavioral treatment effect was identified at the group level while EEG complexity increased in low frequency bands. Electrophysiological changes were not translated into behavioral changes at the group level.

Abstract. Studying emotions has become increasingly popular in various research fields. Researchers across the globe have studied various tools to implicitly assess emotions and affective states of people. Human computer interface systems specifically can benefit from such implicit emotion evaluator module, which can help them determine their users' affective states and act accordingly. Brain electrical activity can be considered as an appropriate candidate for extracting emotion-related cues, but it is still in its infancy. In this paper, the results of analyzing the Electroencephalogram (EEG) for assessing emotions elicited during watching various pre-selected emotional music video clips have been reported. More precisely, in-depth results of both subject-dependent and subjectindependent correlation analysis between time domain, and frequency domain features of EEG signal and subjects' self assessed emotions are produced and discussed.

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Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson's disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.

Sensation of reality refers to the ability of users to feel present in a multimedia experience. As 3D technologies target to provide more immersive and higher quality multimedia experiences, it is important to understand Quality of Experience (QoE) and sensation of reality. Recently, there have been efforts to measure brain activity in order to understand implicitly QoE for various multimedia contents. However, brain activity accounting for sensation of reality has not been adequately investigated. The goal of this paper is twofold. First, we investigate how various aspects, such as perceived quality, perceived depth, and content preference affect subjective sensation of reality through explicit subjective ratings. Second, we construct subjective classification systems to predict sensation of reality from multimedia experiences based on electroencephalography (EEG) and peripheral physiological signals such as heart rate and respiration.

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Abstract-Emotion is a dynamic process that affects social relationships and influences the mechanisms of rational thinking and decision making. The influence of emotion on reasoning has gained a lot of attention in computer science. One approach to incorporating emotions into computers is by extracting features from various physiological signals and inferring the corresponding emotions or emotional dimensions. However, physiological processes are dynamic rather than static, and physiological signals mainly interact with each other during emotional processes rather than act individually. This study suggests that dynamical interdependencies of various signals that emanate from either the peripheral nervous system or the central nervous system might be involved in the emotional experience. Specifically, EEG (electroencephalography) signals captured from the temporal lobe are coupled with a narrow-band skin conductance response (SCR) while subjects are watching music clips, indicating that signals from the auditory cortex interact with SCR during this experience.