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.

Tinnitus (TI) is a disease of the brain with high prevalence and often severe consequences for which no causal therapy approach has been established so far. Neurofeedback Training (NFT) is considered a promising approach to treat TI based on studies applying the Dohrmann-protocol reporting reduced TI loudness and distress. As the current method is relatively laborious and expensive, home-based NFT could make this promising approach accessible to a larger number of patients. However, it is still unclear whether and how NFT can be carried out at home. This study evaluated the feasibility of the Dohrmann-protocol in a home-based, sham-controlled, single blind, longitudinal cross-over wash-out design with$\mathrm{N}=9$TI patients. EEG was recorded during 30 NFT or sham feedback sessions and acceptance of the at-home treatment was measured longitudinally. Ordinary acceptance, especially in response to veritable NFT in comparison to sham feedback and a dropout rate of 22.20 % were observed. Home-based NFT produced impedances$<10 \text{kOhm}$, indicating acceptable EEG contact quality. TI distress was reduced, and NFT increased the alpha delta ratio. We conclude, the feasibility of a methodologically sound home-based NFT study was demonstrated. Limitations discuss the small sample size. Future directions include optimizing hard- and software procedures to enhance system usability and user interaction.

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.