Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

Mixed Reality (MR) technologies have transformed the way in which we interact and engage with digital content, offering immersive experiences that blend the physical and virtual worlds. Over the past years, there has been increasing interest in employing Artificial Intelligence (AI) technologies to improve user experience and trustworthiness in cultural contexts. However, the integration of Large Language Models (LLMs) into MR applications within the Cultural Heritage (CH) domain is relatively underexplored. In this work, we present an investigation into the integration of LLMs within MR environments, focusing on the context of virtual art exhibitions. We implemented a HoloLens MR application, which enables users to explore artworks while interacting with an LLM through voice. To evaluate the user experience and perceived trustworthiness of individuals engaging with an LLM-based virtual art guide, we adopted a between-subject study design, in which participants were randomly assigned to either the LLM-based version or a control group using conventional interaction methods. The LLM-based version allows users to pose inquiries about the artwork displayed, ranging from details about the creator to information about the artwork’s origin and historical significance. This paper presents the technical aspects of integrating LLMs within MR applications and evaluates the user experience and perceived trustworthiness of this approach in enhancing the exploration of virtual art exhibitions. Results of an initial evaluation provide evidence about the positive aspect of integrating LLMs in MR applications. Findings of this work contribute to the advancement of MR technologies for the development of future interactive personalized art experiences.

Nowadays, through the Internet, cultural heritage reaches broader audiences via digital platforms that manage and disseminate cultural content formulating a common sense in societies and supporting society’s cohesion. Various cultural heritage digital platforms embraced the idea of user participation in contributing cultural content. In this work, we evaluate the usability and the personal and social acceptance of a specific participatory platform (Culture Gate) that manages, disseminates and exploits cultural content and services. We associate platform usability with the level of engagement platform’s content and services bring on users. Personal acceptance is related to specific factors like the users’ disposal to disseminate and exploit platform content and services or to actively participate in a digital community. We relate social acceptance to specific factors like the promotion of users’ responsibility towards the online community and the society, the support of intergenerational dialogue and the amplification of a user’s sense of belonging to a society. Evaluation methodology applies a research model based on hypotheses, deriving from the ideas of widely accepted sociological and economic theories, which reflect each of the aforementioned factors. Evaluation results suggest that the test-bed platform is considered usable and acceptable, in a personal and social level, by the users.