This study presents the first phase of a transdisciplinary research project aimed at improving the design of visual feedback stimuli in neurofeedback (NFB) applications. While current NFB research has focused extensively on signal processing and feature extraction, limited attention has been given to the design and user experience of feedback stimuli. To address this gap, the research team conducted generative user research including site visits, expert consultations, and semistructured interviews with domain experts and previous NFB participants. Analysis of the collected data yielded a preliminary set of design requirements. User-centered requirements include minimizing cognitive load, enhancing attention and engagement, incorporating positive reinforcement, supporting a sense of agency, and providing clear instructions. Technical requirements include reducing artifacts, ensuring low-latency feedback, and promoting participant relaxation. These findings lay the groundwork for iterative design and evaluation phases, with the ultimate goal of delivering validated stimuli and design guidelines to the NFB research and clinical communities.

The Advancing Neurofeedback in Tinnitus (ANT) project aims to develop improved neurofeedback protocols and BCI technology by systematically designing engaging feedback stimuli and optimizing neural targets. General design principles for audiovisual feedback stimuli are established, system and software engineering for general purpose real-time M/EEG is developed, and ultimately integrated for the clinical use case tinnitus. This interdisciplinary effort combines expertise in clinical neuroscience, design, user experience research, psychology, and biomedical signal processing to create a novel neurofeedback approach with potential for both clinical and home-based applications.

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algorithms spanning traditional univariate metrics, such as frequency band power and entropy, to advanced bivariate connectivity measures. It is compatible with various recording systems, enabling the extraction of neural targets from brain signals in real time, with potential applications in enhancing neurofeedback efficacy.

This study investigates patient's perspective on the use of AI in healthcare and the role of Explainable AI in this context. Through a co-creative workshop with six participants from diverse disciplines, we investigated the impact of transparency on trust. The findings highlight parallels between AI and doctors as “black boxes,” the complexity of informed consent and the importance of emotional safety. This work serves as a starting point for ongoing research that engages diverse stakeholder groups, to ensure the development of usercentered XAI solutions that can be effectively implemented in clinical practice.