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.

Residual inhibition (RI) is a phenomenon observed in many tinnitus patients, where tinnitus remains temporarily suppressed for a short duration—typically less than a minute-after the cessation of an appropriate masking stimulus. Despite decades of clinical interest in RI, machine learning (ML)-based, feature-driven classification approaches remain scarce. In this study, we investigate the potential of ML models to classify RI by developing a dedicated data analysis pipeline. Given the heterogeneous nature of the features—including numerical, binary, and ordinal variables-we apply feature importance techniques tailored for mixed-type data to ensure a comprehensive evaluation and improve interpretability. Our results demonstrate a clear separation between RI classes, highlighting the relevance of specific clinical and audiological factors in distinguishing them. Building on this, we assess the predictive power of RI classifications with high confidence within a supervised learning framework to determine their relevance for treatment outcome prediction. While our findings confirm that RI can be effectively classified, they also suggest that RI alone is not sufficient to serve as a reliable predictor for treatment outcomes.

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.

Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and vary over time. Usually, in interviews or self-report questionnaires, patients are asked to report symptoms as well as their severity and duration retrospectively. However, only little is known to what degree such retrospective reports reflect the symptoms experienced in daily life some time ago. Mobile technologies can help to bridge this gap: mobile self-help services allow patients to record their symptoms prospectively when (or shortly after) they occur in daily life. In this study, we present results that we obtained with the mobile crowdsensing platform TrackYourTinnitus to show that there is a discrepancy between the prospective assessment of symptom variability and the retrospective report thereof. To be more precise, we evaluated the real-time entries provided to the platform by individuals experiencing tinnitus. The results indicate that mobile technologies like the TrackYourTinnitus crowdsensing platform may go beyond the role of an assistive service for patients by contributing to more accurate diagnosis and, hence, to a more elaborated treatment.