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.

As public displays become more ubiquitous, common authentication methods face security (e.g., shoulder surfing) and sanitary risks. Gaze-based systems offer a promising alternative, but their adoption is often limited by required calibration and the Midas Touch problem. We present EyeGestureLogin, a touch-free, knowledge-based method that uses gaze to enter lock patterns on a familiar 3 × 3 layout. EyeGestureLogin supports spontaneous walk-up use by replacing explicit calibration with an implicit, single-point offset estimation, and mitigates the Midas Touch using a short dwell-time trigger for fully hands-free input. The interface captures patterns by processing AOI-based fixations; we additionally evaluate an offline saccade-based detector. In a controlled study (n = 24), we achieved 91.59% accuracy (91.88% offline) with a mean entry time of 4.36 s. These results suggest that EyeGestureLogin enables fast and accurate hands-free authentication for public displays, motivating further evaluation under real-world deployment conditions.

Training image classification models requires large labelled datasets, which is particularly challenging in specialised domains where manual expert annotation remains the default, such as eye tracking. To address this challenge, we present Train the Spire, a single-player game with a purpose (GWAP) that embeds image annotation within turn-based card game mechanics for crowdsourcing data annotations. The system uses a few-shot deep learning classifier to validate player-generated labels, providing immediate feedback through rewards and penalties. The game incorporates elements, such as progression systems, a companion agent for system transparency, and balanced difficulty, to maintain player engagement while ensuring annotation accuracy. In this paper, we present the system design, implementation details, and evaluation study design for comparing Train the Spire against a baseline annotation tool using the VISUS mobile eye-tracking dataset in an online user study measuring effectiveness, usability, and enjoyment.

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.

Abstract 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.

Games are used in multiple fields of brain–computer interface (BCI) research and applications to improve participants’ engagement and enjoyment during electroencephalogram (EEG) data collection. However, despite potential benefits, no current studies have reported on implemented games for Speech Imagery BCI. Imagined speech is speech produced without audible sounds or active movement of the articulatory muscles. Collecting imagined speech EEG data is a time-consuming, mentally exhausting, and cumbersome process, which requires participants to read words off a computer screen and produce them as imagined speech. To improve this process for study participants, we implemented a maze-like game where a participant navigated a virtual robot capable of performing five actions that represented our words of interest while we recorded their EEG data. The study setup was evaluated with 15 participants. Based on their feedback, the game improved their engagement and enjoyment while resulting in a 69.10% average classification accuracy using a random forest classifier.

A scanpath is an important concept in eye tracking that represents a person’s eye movements in a graph-like structure. Passive gaze-based interfaces, in which users do not consciously interact using their eyes, typically interpret users’ scanpaths to enable adaptive and personalised interaction. Despite the benefits of graph neural networks (GNNs) in graph processing, this technology has not been considered for that purpose. An example application is perceived relevance estimation, which still suffers from low classification performance. In this work, we investigate how and whether GNNs can be used to analyse scanpaths for readers’ perceived relevance estimation using the gazeRE dataset. This dataset contains eye tracking data from 24 participants, who rated the relevance of 12 short and 12 long documents in relation to a given query. The relevance was assigned either to an entire short document or to each paragraph within a long document, which allowed us to investigate two different GNN tasks. For comparison, we reproduced the gazeRE baseline using Random Forest and Support Vector classifiers, and an additional Convolutional Neural Network (CNN) from the literature. All models were evaluated using leave-users-out cross-validation. For short documents, the GNNs surpassed the baseline methods, with certain experiments showing an absolute balanced accuracy improvement of 7.6% and 14.3% over the CNN and gazeRE baselines, respectively. However, similar improvements were not observed in long documents. This work investigates and discusses the future potential of using GNNs as a scanpath analysis method for passive gaze-based applications, such as implicit relevance estimation.

Silent speech Brain-Computer Interfaces (BCIs) try to decode imagined speech from brain activity. Those BCIs require a tremendous amount of training data usually collected during mentally and physically exhausting sessions in which participants silently repeat words presented on a screen for several hours. Within this work we present an approach to overcome those exhausting sessions by training a silent speech classifier on data recorded while speaking certain words and transferring this classifier to EEG data recorded during silent repetition of the same words. This approach does not only allow for a less mentally and physically exhausting training procedure but also for a more productive one as the overt speech output can be used for interaction while the classifier for silent speech is trained simultaneously. We evaluated our approach in a study in which 15 participants navigated a virtual robot on a screen in a game like scenario through a maze once with 5 overtly spoken and once with the same 5 silently spoken command words. In an offline analysis we trained a classifier on overt speech data and let it predict silent speech data. Our classification results do not only show successful results for the transfer (61.78%) significantly above chance level but also comparable results to a standard silents speech classifier (71.48%) trained and tested on the same data. These results illustrate the potential of the method to replace the currently tedious training procedures for silent speech BCIs with a more comfortable, engaging and productive approach by a transfer from overt to silent speech.