Michael Barz

Event detection (e.g., fixations and saccades) is a prerequisite for many eye-tracking analyses. However, existing solutions often require coding experience, rely on closed-source vendor tools, or focus on narrow paradigms (e.g., reading). Additionally, most are designed for stationary eye tracking, whereas head-mounted recordings are affected by head/scene motion, making event detection more difficult. Therefore, we present OpenGazeLab, a publicly available, browser-based toolkit that unifies event extraction, parameter configuration, and data inspection for both stationary and head-mounted eye tracking. OpenGazeLab implements I-DT and I-VT, and extends them for head-mounted data using scene-motion compensation and adaptive thresholds. The toolkit also provides a timeline-based visualisation that overlays gaze and detected events on the stimulus image or scene video, enabling quick visual verification. OpenGazeLab is implemented using widely used, well-maintained Python frameworks to support reproducible, easy-to-adopt workflows, and we plan to extend it with additional event classes (e.g., smooth pursuit) and alternative detectors.

The widespread use of generative AI has led to increased focus on human–AI interaction. However, AI systems can generate unexpected outputs, leading to disagreement or human–AI conflict. This paper focuses on modelling user disagreement using machine learning (ML) by observing users’ implicit viewing behaviour. We conducted a controlled study with 30 participants evaluating captions from a simulated ML image-captioning system. Participants indicated agreement or disagreement with each caption while we recorded their gaze and facial-expression data, which we used to predict (dis)agreement. We show that unimodal gaze-based personalised modelling (0.684 average balanced accuracy) outperforms generalised modelling (0.570), whereas multimodal approaches did not improve performance. Our exploratory post hoc gaze-based analysis highlights the importance of feature selection and temporal dynamics, which help guide system design and future work. We release the dataset to support reproducibility and further work. Due to the nature of this research, we also discuss the potential ethical and privacy implications of continuous passive gaze and facial monitoring.

PurposeCentralized machine learning often struggles with limited data access and expert involvement. We investigate decentralized approaches that preserve data privacy while enabling collaborative model training for medical imaging tasks.ApproachWe explore asynchronous federated learning (FL) using the FL with buffered asynchronous aggregation (FedBuff) algorithm for classifying optical coherence tomography (OCT) retina images. Unlike synchronous algorithms such as FedAvg, which require all clients to participate simultaneously, FedBuff supports independent client updates. We compare its performance to both centralized models and FedAvg. In addition, we develop a browser-based proof-of-concept system using modern web technologies to assess the feasibility and limitations of interactive, collaborative learning in real-world settings.ResultsFedBuff performs well in binary OCT classification tasks but shows reduced accuracy in more complex, multiclass scenarios. FedAvg achieves results comparable to centralized training, consistent with previous findings. Although FedBuff underperforms compared with FedAvg and centralized models, it still delivers acceptable accuracy in less complex settings. The browser-based prototype demonstrates the potential for accessible, user-driven FL systems but also highlights technical limitations in current web standards, especially regarding local computation and communication efficiency.ConclusionAsynchronous FL via FedBuff offers a promising, privacy-preserving approach for medical image classification, particularly when synchronous participation is impractical. However, its scalability to complex classification tasks remains limited. Web-based implementations have the potential to broaden access to collaborative AI tools, but limitations of the current technologies need to be further investigated.

Integrating eye-tracking technologies in Extended Reality (XR) headsets has enabled intuitive, hands-free system interaction, such as gaze-based menu navigation. However, there is a lack of comprehensive comparisons and consensus in the literature on the optimal use of gaze-based menu navigation. This paper presents a comparative analysis of gaze-based menu navigation in virtual environments, focusing on two common menu layouts: pie and list menus, with three interaction methods: gaze-based dwell, controller-based, and a multimodal approach combining gaze and controller inputs. We conducted a 19-participant within-subject study, measuring task completion time, error rate, usability, and user preference for each condition. The results indicate that while the pie layout was statistically faster and less erroneous than the list layout, novice users tend to favour list layouts. Furthermore, we found that users preferred the multimodal interaction method, despite its lower task completion times and higher error rates compared to controller-based navigation. Based on our findings, we offer design guidelines and recommendations for implementing gaze-based menu systems.

Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals.