Open Access

This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and motor imagery (MI). FNNs represent a neural network architecture tailored to smooth processes, and as such have been applied to EEG data classification recently. The paper proposes a comprehensive pipeline encompassing data acquisition, synchronization, pre-processing, training of FNNs, and real-time inference to enable the seamless integration of FNNs into real-world BCI applications. For the first time, FNNs are integrated into an end-to-end pipeline and serve for live inference outside a strict laboratory setting. In pursuit of a more accessible electroencephalography (EEG) artificial intelligence (AI) training scenario, the paper introduces a self-paced environment for auto-labeling EEG data. A customdesigned Pong game serves as the training task and enables subjects to engage in MI/ME tasks while receiving immediate visual feedback. To automate the labeling process of the recorded EEG data, the movements of both arms are captured with inertial measurement units (IMUs) and analyzed through gesture recognition. This novel training framework contributes to more natural and engaging data collection and reduces pre-processing for model training. To provide a comprehensive evaluation, the paper compares the performance of FNN and EEGNet in the self-paced MI/ME tasks. The comparative analysis addresses factors such as classification accuracy, real-time processing speed, and power consumption. Furthermore, the study explores various auto-labeling methods within the self-paced environment, analyzing their impact on the classification performances of both architectures. By evaluating these labeling methods, this work addresses the challenge of accurate and efficient EEG data labeling, crucial for training robust models for prediction of time critical events. The proposed pipeline and experimental design culminate in a full-scale evaluation of the FNN-based classification system to demonstrate its efficacy in real-time MI/ME tasks. The paper’s contributions not only establish FNNs as a potent tool for EEG classification but also provide valuable insights into enhancing the accessibility, usability, and performance of EEG-based AI systems in real-world applications.

The portmanteau test provides the vanilla method for detecting serial correlations in classical univariate time series analysis. The method is extended to the case of observations from a locally stationary functional time series. Asymptotic critical values are obtained by a suitable block multiplier bootstrap procedure. The test is shown to asymptotically hold its level and to be consistent against general alternatives.

Background: The aim of this study was to assess the agreement between different analysis protocols for the determination of retinal vessel dilation response to flicker light (FL) and its relation to static and metabolic parameters of retinal vessels in healthy subjects. Methods: In total, 24 right eyes of 24 healthy controls (mean age: 36.04 ± SD 14.4 years) who underwent dynamic and static retinal diameter and oxygen saturation measurements on a Retinal Vessel Analyzer (RVA, Imedos, Jena, Germany) were included. Using repeated video analyses, responses to FL were measured with RVA. These measurements were conducted at three specific retinal locations: within the superotemporal area—within a distance of less than one optic disk (OD) diameter to optic nerve head (ONH) (group 1); greater than one OD diameter to ONH (group 2); and areas near the ONH within the VesselMap region (group 3). For comparability, the static and oxygen saturation parameters were also calculated in the superotemporal peripapillary area using the VesselMap tool of the RVA and were evaluated in relation to the corresponding dynamic area (group 3). Results: In all groups, the vascular FL response of arteries was less pronounced compared to venules (p = 0.0014). Even though FL responses (mean ± SD: FL-A; FL-V) in group 1 were more pronounced (3.36 ± 2.31; 4.42 ± 1.69) compared to those in group 2 (2.97 ± 2.40; 4.08 ± 1.55) and group 3 (2.84 ± 2.29; 4.21 ± 2.03), they did not reach statistically significant values. The mean flicker response of venules (VDil) in all groups showed negative correlations to the corresponding static parameter: central retinal venous equivalent (CRV) (r = −0.0437; p = 0.015). The mean flicker response of arteries (ADil) in all groups showed negative correlations to the corresponding metabolic parameter: arterio-venous oxygen extraction fraction (r = −0.101; p = 0.041). Conclusions: Our study confirms that the flicker light response, despite slight variations in its duration and location, allows for reliable measurements, proving the Retinal Vessel Analyzer to be a valuable diagnostic tool. Furthermore, we were able to highlight the relationship between the dynamic and metabolic components of retinal supply, which enables early diagnosis concerning the development of diseases within this spectrum.

This study evaluated Lolium perenne press juice as a sustainable substrate for Single-Cell Protein (SCP) production using Kluyveromyces marxianus. Key fermentation parameters were systematically optimized, including microbial reduction, dilution ratios, temperature, and nutrient supplementation. Pasteurization at 75 °C preserved essential nutrients better than autoclaving, resulting in a 27.8% increase in biomass yield. A 1:2 dilution of press juice enhanced fermentation efficiency, achieving 20.2% higher biomass despite a lower initial sugar content. Cultivation at 30 °C enabled sustained substrate utilization and outperformed 40 °C fermentation, increasing final biomass by 43.4%. Nutrient supplementation with yeast extract, peptone, and glucose led to the highest biomass yield, with a 71% increase compared to unsupplemented juice. Press juice from the tetraploid variety, Explosion, consistently outperformed the diploid Honroso, especially when harvested early, reaching up to 16.62 g·L−1 biomass. Early harvests promoted faster growth, while late harvests exhibited higher biomass yield coefficients due to improved sugar-to-biomass conversion. Compared to a conventional YM medium, fermentation with L. perenne press juice achieved up to a threefold increase in biomass yield. These findings highlight the potential of grass-based substrates for efficient SCP production and demonstrate how agricultural parameters like variety and harvest timing influence both quantity and quality. The approach supports circular bioeconomy strategies by valorising underutilized biomass through microbial fermentation.

The High Brilliance neutron Source (HBS) project pioneers a High-Current Accelerator-Based Neutron Source (HiCANS), optimizing elements for tailored neutron production. HBS adapts pulse structure and moderators to instrument requirements, focusing on the Target-Moderator-Reflector (TMR) system. Recognizing the need for multiple channels, Monte Carlo simulations are used to compare different models’ influence on neutron brilliance and beam divergence.