Modeling Immersive Media Experiences by Sensing Impact on Subjects

As immersive technologies target to provide higher quality of multimedia experiences, it is important to understand the quality of experience (QoE) perceived by users from various multimedia rendering schemes, in order to design and optimize human-centric immersive multimedia systems. In this study, various QoE-related aspects, such as depth perception, sensation of reality, content preference, and perceived quality are being investigated and compared for presentation of 2D and 3D contents. Since the advantages of implicit over explicit QoE assessment have become essential, the way these QoE-related aspects influence brain and periphery is also investigated. In particular, two classification schemes using electroencephalography (EEG) and peripheral signals (electrocardiography and respiration) are carried out, to explore if it is possible to automatically recognize the QoE-related aspects under investigation. In addition, a decision-fusion scheme is applied to EEG and peripheral features, to explore the advantage of integrating information from the two modalities. The results reveal that the highest monomodal average informedness is achieved in the high beta EEG band (0.14% +/- 0.09, p<0.01), when recognizing sensation of reality. The highest and significantly non-random multimodal average informedness is achieved in when high beta EEG band is fused with peripheral features (0.17% +/- 0.1, p<0.01), for the case of sensation of reality. Finally, a temporal analysis is conducted to explore how the EEG correlates for the case of sensation of reality change over time. The results reveal that that the right cortex is more involved when sensation of reality is low, and the left one when sensation of reality is high, indicating that approach and withdrawal-related processes occur during sensation of reality

Predicting Subjective Sensation of Reality During Multimedia Consumption Based on EEG and Peripheral Physiological Signals

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

Multimodal Dataset for Assessment of Quality of Experience in Immersive Multimedia

This paper presents a novel multimodal dataset for the analysis of Quality of Experience (QoE) in emerging immersive multimedia technologies. In particular, the perceived Sense of Presence (SoP) induced by one-minute long video stimuli is explored with respect to content, quality, resolution and sound reproduction, and annotated with subjective scores. Furthermore, a complementary analysis of the acquired physiological signals, such as Electroencephalography (EEG), Electrocardiography (ECG), and respiration is carried out, aiming at an alternative evaluation of human experience while consuming immersive multimedia. Presented results conrm the value of the introduced dataset and its consistency for the purposes of QoE assessment for immersive multimedia. More specically, subjective ratings demonstrate that the created dataset enables distinction between low and high levels of immersiveness, which is also conrmed by a preliminary analysis of recorded physiological signals

The Religious Implications of Fishing and Bullfighting in Hemingway’s Work

On Hemingway’s metaphoric treatment of redemption in fishing and bullfighting passages from several novels and short stories, including The Sun Also Rises, Men at War, Death in the Afternoon, and “Big Two-Hearted River.

Non-Linear EEG Features for Odor Pleasantness Recognition

Since olfactory sense is gaining ground in multimedia applications, it is important to understand the way odor pleasantness is perceived. Although several studies have explored the way odor pleasantness perception influences the power spectral density of the electroencephalography (EEG) in various brain regions, there are still no studies that investigate the way odor pleasantness perception affects the non-linear temporal variations of EEG. In this study two non-linear metrics are used, namely permutation entropy, and dimension of minimal covers, to explore the possibility of recognizing odor pleasantness perception from the non-linear properties of EEG signals. The results reveal that it is possible to discriminate between pleasant and unpleasant odors from the EEG nonlinear properties, using a Linear Discriminant Analysis classifier with cross-validation

Subject-Independent Odor Pleasantness Classification Using Brain and Peripheral Signals

Enhanced sensation of reality from multimedia contents can be achieved by creating realistic multimedia environments, using visual, auditory, and olfactory information. Although the affective information from video and audio has been extensively studied, the olfactory sense has received less attention. A way to assess human experience from audio, video or odors, is by investigating physiological signals. In this study, 23 subjects experienced pleasant, unpleasant, and neutral odors while their electroencephalogram (EEG), and electrocardiogram (ECG) were recorded. Two independent three-class classifiers were trained and tested, using EEG or ECG features. The results reveal a significant increase in the classification performance when EEG features were used (Cohen's kappa k = 0.44 +/- 0.14; p < 0.001). The results also indicate that it is possible to automatically classify the perception of unpleasant odors using EEG signals, but the classification performance decreases significantly when classifying between pleasant and neutral odors. Among the EEG features, the Wasserstein distance metric estimated between trial and baseline power achieved the highest classification performance. Features from ECG signals did not result in a significantly non-random performance