Cognitator: Brain-Controlled Virtual Reality Game with Unicorn Hybrid Black @gtecmedicalengineering

Uploaded October 2025 | Updated September 2026, 1 day ago

BR41N.IO Hackathon during IEEE SMC 2025 - First place winner - BCI Gaming

Can a virtual reality game be controlled directly by brain activity? At the BR41N.IO Hackathon, an interdisciplinary team of neuroscientists, Unity developers, and artists created Cognitator, a real-time Brain-Computer Interface (BCI) adventure game that combines the Unicorn Hybrid Black EEG headset with the Meta Quest VR platform.

Developed within just 24 hours, the project demonstrates how rapidly developers can build immersive neurotechnology applications using real-time EEG acquisition, virtual reality, Unity development, Python signal processing, and the Unicorn ecosystem. The team streamed raw EEG data from the Unicorn Hybrid Black using Lab Streaming Layer (LSL) and integrated multiple Brain-Computer Interface paradigms directly into gameplay.

The game combines several EEG-based control methods. Players use alpha band neurofeedback to trigger relaxation-based events, EMG activity generated through jaw clenching to perform actions within the game, and code-modulated Visual Evoked Potentials (cVEPs) to select and interact with virtual objects. By combining neurofeedback, cognitive neuroscience, real-time EEG processing, and advanced BCI paradigms, the team created a fully interactive virtual reality experience controlled by brain and physiological signals.

One of the most impressive aspects of the project is that the team was able to focus on game mechanics, user experience, signal classification, and Brain-Computer Interface development instead of spending valuable hackathon time on EEG hardware integration, data acquisition, artifact handling, or low-level software infrastructure. The Unicorn Hybrid Black provided reliable real-time EEG acquisition, direct access to raw EEG data, LSL streaming, and a development-ready platform that enabled rapid prototyping.

Cognitator showcases several important applications of modern neurotechnology, including virtual reality, Brain-Computer Interfaces, neurofeedback, cognitive neuroscience, human-computer interaction, EEG gaming, real-time neuroscience, and multimodal signal processing. The project also demonstrates how EEG, EMG, machine learning, Unity development, and immersive environments can be combined to create next-generation interactive experiences.

Projects like Cognitator demonstrate why the Unicorn Hybrid Black is widely used for BCI research, ERP research, SSVEP and VEP applications, neurofeedback, Python-based EEG development, Unity integration, cognitive neuroscience, and neurotechnology prototyping. By providing high-quality EEG signals, real-time data access, open APIs, LSL support, and a complete software ecosystem, the platform enables developers to move from concept to working Brain-Computer Interface applications in hours rather than weeks.

BR41N.IO Hackathon projects repeatedly show that researchers, students, startups, and developers can build real-time EEG applications, virtual reality experiences, Brain-Computer Interfaces, robotics systems, assistive technologies, machine learning solutions, and neurotechnology products within 24 hours using the Unicorn Hybrid Black platform.

More about Unicorn Hybrid Black: https://www.gtec.at/product/unicorn-hybrid-black-bci-platform
More about BR41N.IO: https://www.gtec.at/hackathon/

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$Unlockers: High-Accuracy P300 Classification for Brain-Computer Interface Communication BR41N.IO Hackathon during IEEE SMC 2025 - Second place winner - Data Analysis How can Brain-Computer Interfaces become more practical for locked-in patients when only limited training data is available? At the BR41N.IO Hackathon, Team Unlockers developed an advanced machine learning pipeline designed to improve P300 classification accuracy while reducing the amount of data required from users. Built within just 24 hours, the project focused on one of the most important challenges in BCI research and assistive communication: achieving reliable P300 detection without requiring hundreds of repetitive trials. This is especially important for locked-in patients, where lengthy calibration sessions can be physically demanding and limit practical use. The team developed a complete EEG signal processing and machine learning workflow that combined artifact removal, data augmentation, feature engineering, and advanced classification techniques. Multiple Linear Discriminant Analysis (LDA) approaches were evaluated, including Fishers LDA, Regularized LDA, and Stepwise Regularized LDA, alongside several EEG preprocessing strategies designed to maximize signal quality while preserving valuable training data. A key innovation of the project was the use of targeted EEG data augmentation techniques, including peak shifting, amplitude scaling, and temporal transformations to generate additional training examples from limited datasets. The team also explored spiking neural networks for synthetic EEG generation, demonstrating how artificial intelligence and machine learning can improve Brain-Computer Interface performance when training data is scarce. The final classification pipeline achieved an impressive 94% accuracy for P300 versus non-P300 detection, outperforming many traditional baseline approaches while requiring only a fraction of the training data often used in BCI systems. These results highlight the potential for more efficient calibration procedures, faster user onboarding, and improved patient experience in assistive communication applications. This project demonstrates important concepts in ERP research, P300 spellers, cognitive neuroscience, assistive communication, machine learning, artificial intelligence, EEG signal processing, and Brain-Computer Interface development. It also shows how rapidly interdisciplinary teams can build and validate advanced neurotechnology solutions when they can focus on algorithms and innovation rather than hardware integration and signal acquisition challenges. BR41N.IO Hackathon projects repeatedly demonstrate that researchers, students, startups, and developers can build high-performance Brain-Computer Interfaces, ERP applications, machine learning pipelines, assistive technologies, and real-time EEG solutions within just 24 hours. The Unicorn Hybrid Black platform enables teams to focus on classification, signal analysis, and application development instead of spending valuable time on EEG hardware setup, artifact correction workflows, or low-level data acquisition. More about Unicorn Hybrid Black: https://www.gtec.at/product/unicorn-hybrid-black-bci-platform More about BR41N.IO: https://www.gtec.at/hackathon/$

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