Lukas Röhrling, Selina Breuer, Carina Arnberger, Christoph Aigner, Thomas Grechenig, René Baranyi
A multilayer perceptron neural network used with six clinical inputs demonstrated moderate performance in identifying SNI among pediatric ECMO patients; integration with EEG-derived spectral features did not significantly enhance the performance. However, this conclusion is highly exploratory and dependent on selected EEG inputs, thus needing to be valid with a larger patient cohort, appropriate EEG feature selections, and assess cross-site generalizability.
Attention-deficit/hyperactivity disorder (ADHD) involves difficulties in sustaining attention and resisting distraction. This has motivated the development of feedback-driven environments for cognitive control training. Integrating electroencephalography (EEG) sensors into Virtual Reality (VR) serious games for cognitive therapy remains relatively underexplored and requires reliable, non-invasive brain-computer interfaces. The existing solutions use multi-channel systems that primarily suffer from requiring complex hardware, while not combining motor imagery (MI) with concentration levels. Therefore, this case study evaluates the feasibility and data quality of a lightweight, cost-effective sensor configuration for real-time control of mental state. A non-invasive, eight-channel OpenBCI Cyton board was integrated with an EEG cap using the international 10-20 placement system, alongside a Meta Quest 2 headset, to capture MI and concentration signals directly from the user's scalp. Signal acquisition was hindered by high impedance and channel railing, which required conductive gel mitigation, while mechanical tension from the VR headset strap introduced motion artifacts and noise. Nevertheless, under stable signal conditions, the optimized eight-channel sensor setup achieved a subject-specific online classification accuracy of up to 90% using the deep learning model "EEGNet". The findings demonstrate the technical feasibility of acquiring and classifying EEG activity using a low-cost eight-channel sensor configuration in an interactive VR-BCI Serious Gaming application, provided that skin-electrode impedance and mechanical sensor interferences are managed. The results provide a basis for future investigation of such systems in cognitive-training applications, while further studies, including clinical evaluations, are required to assess their applicability in therapeutic contexts.