Urban green spaces are critical for well-being, yet planners lack scalable ways to anticipate how environments will be perceived by users. We conducted an experiment with 27 participants who viewed 30 images of urban spaces while eye movements and brain activity were recorded. Image composition, parsed into 14 urban classes and aggregated as vegetation versus non-vegetation, systematically predicted responses: a higher proportion of vegetation drew more visual attention and was associated with higher attractiveness ratings, while images with less greenery elicited stronger pupillary responses. Brain signal analysis showed topographic patterns in theta and alpha activity between pleasant and unpleasant scenes, although differences were not statistically significant. Taken together, our findings highlight systematic links between urban scene composition, user attention, and affective responses. We release our dataset and software to support further research.

Visual Exposure Art Therapy (VEAT) is commonly delivered using static physical or flat-screen media, limiting immersion and adaptive personalization. We present IEDAT, an immersive virtual reality system that combines AI-driven artwork recommendation with therapist-guided, human-in-the-loop personalization. Therapy sessions are delivered in a virtual museum, enabling adaptive exposure while preserving professional control and transparency. In an in-situ study with 58 participants, we compared IEDAT to a state-of-the-art desktop VEAT baseline. Outcomes were evaluated using self-reported negative affect, reflection analysis, and neurophysiological measures including functional near-infrared spectroscopy (fNIRS; prefrontal regional homogeneity, ReHo) and EEG band power. While both modalities reduced negative affect, the immersive condition elicited richer recovery-oriented meaning-making in participant reflections. Supportive neurophysiological patterns further contextualized these experiential differences: VR showed increased post-session prefrontal ReHo and a directional increase in low-frequency (delta-theta) EEG power. Together, these findings demonstrate the value of immersive, human-in-the-loop personalization for strengthening engagement in digital art therapy and highlight the role of multimodal evaluation in personalized well-being systems.

Effective decision-making often relies on timely insights from complex visual data. While Information Visualization (InfoVis) dashboards can support this process, they rarely adapt to users’ cognitive state, and less so in real time. We present SYMBIOTIK, an intelligent, context-aware adaptive visualization system that leverages neurophysiological signals to estimate mental workload (MWL) and dynamically adapt visual dashboards using reinforcement learning (RL). Through a user study with 120 participants and three visualization types, we demonstrate that our approach improves task performance and engagement. SYMBIOTIK offers a scalable, real-time adaptation architecture, and a validated methodology for neuroadaptive user interfaces.

Information Visualization (InfoVis) systems utilize visual representations to enhance data interpretation. Understanding how visual attention is allocated is essential for optimizing interface design. However, collecting Eye-tracking (ET) data presents challenges related to cost, privacy, and scalability. Computational models provide alternatives for predicting gaze patterns, thereby advancing InfoVis research. In our study, we conducted an ET experiment with 40 participants who analyzed graphs while responding to questions of varying complexity within the context of digital forensics. We compared human scanpaths with synthetic ones generated by models such as DeepGaze, UMSS, and Gazeformer. Our research evaluates the accuracy of these models and examines how question complexity and number of nodes influence performance. This work contributes to the development of predictive modeling in visual analytics, offering insights that can enhance the design and effectiveness of InfoVis systems.

Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt speech data to covert speech classification. We used electroencephalogram (EEG) features derived from the Hilbert envelope and temporal fine structure, and used them to train a bidirectional long-short-term memory (BiLSTM) model for classification. Our method reduces the burden of extensive training and achieves state-of-the-art classification accuracy: 86.44% for overt speech and 79.82% for covert speech using the overt speech classifier.

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extrinsic physical characteristics such as locomotion and pose can be accurately tracked in real-time, reliably capturing mental states is more challenging. Quantitative psychology allows considering more intrinsic features like emotion, attention, or cognitive load. Time perception, in particular, is strongly tied to users' mental states, including stress, focus, and boredom. However, research on objectively measuring the pace at which we perceive the passage of time is scarce. In this work, we investigate the potential of electroencephalography (EEG) as an objective measure of time perception in VR, exploring neural correlates with oscillatory responses and time-frequency analysis. To this end, we implemented a variety of time perception modulators in VR, collected EEG recordings, and labeled them with overestimation, correct estimation, and underestimation time perception states. We found clear EEG spectral signatures for these three states, that are persistent across individuals, modulators, and modulation duration. These signatures can be integrated and applied to monitor and actively influence time perception in VR, allowing the virtual environment to be purposefully adapted to the individual to increase immersion further and improve user experience. A free copy of this paper and all supplemental materials are available at https://vrarlab.uni.lu/pub/brain-signatures.

Affective computing has potential to enrich the development lifecycle of Graphical User Interfaces (GUIs) and of intelligent user interfaces by incorporating emotion-aware responses. Yet, affect is seldom considered to determine whether a GUI design would be perceived as good or bad. We study how physiological signals can be used as an early, effective, and rapid affective assessment method for GUI design, without having to ask for explicit user feedback. We conducted a controlled experiment where 32 participants were exposed to 20 good GUI and 20 bad GUI designs while recording their eye activity through eye tracking, facial expressions through video recordings, and brain activity through electroencephalography (EEG). We observed noticeable differences in the collected data, so we trained and compared different computational models to tell good and bad designs apart. Taken together, our results suggest that each modality has its own “performance sweet spot” both in terms of model architecture and signal length. Taken together, our findings suggest that is possible to distinguish between good and bad designs using physiological signals. Ultimately, this research paves the way toward implicit evaluation methods of GUI designs through user modeling.