Synthesizing affective neurophysiological signals using generative models: A review paper.

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

Sitting or Standing in VR: About Comfort, Conflicts, and Hazards.

This article examines the choices between sitting and standing in virtual reality (VR) experiences, addressing conflicts, challenges, and opportunities. It explores issues such as the risk of motion sickness in stationary users and virtual rotations, the formation of mental models, consistent authoring, affordances, and the integration of embodied interfaces for enhanced interactions. Furthermore, it delves into the significance of multisensory integration and the impact of postural mismatches on immersion and acceptance in VR. Ultimately, the article underscores the importance of aligning postural choices and embodied interfaces with the goals of VR applications, be it for entertainment or simulation, to enhance user experiences.

CAEVR: Biosignals-Driven Context-Aware Empathy in Virtual Reality.

There is little research on how Virtual Reality (VR) applications can identify and respond meaningfully to users' emotional changes. In this paper, we investigate the impact of Context-Aware Empathic VR (CAEVR) on the emotional and cognitive aspects of user experience in VR. We developed a real-time emotion prediction model using electroencephalography (EEG), electrodermal activity (EDA), and heart rate variability (HRV) and used this in personalized and generalized models for emotion recognition. We then explored the application of this model in a context-aware empathic (CAE) virtual agent and an emotion-adaptive (EA) VR environment. We found a significant increase in positive emotions, cognitive load, and empathy toward the CAE agent, suggesting the potential of CAEVR environments to refine user-agent interactions. We identify lessons learned from this study and directions for future work.

VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research.

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches would certainly benefit from an accurately labeled, real-world, diverse dataset that enables the development of generalizable ML models. We introduce 'VR.net', a dataset comprising 165-hour gameplay videos from 100 real-world games spanning ten diverse genres, evaluated by 500 participants. VR.net accurately assigns 24 motion sickness-related labels for each video frame, such as camera/object movement, depth of field, and motion flow. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we implement a tool to automatically and precisely extract ground truth data from 3D engines' rendering pipelines without accessing VR games' source code. We illustrate the utility of VR.net through several applications, such as risk factor detection and sickness level prediction. We believe that the scale, accuracy, and diversity of VR.net can offer unparalleled opportunities for VR motion sickness research and beyond.We also provide access to our data collection tool, enabling researchers to contribute to the expansion of VR.net.