Uploaded October 2025 | Updated September 2026, 1 day ago
BR41N.IO Hackathon during IEEE SMC 2025 - Third Place winner - BCI Gaming
Can a user navigate a maze using nothing but brain signals? At the BR41N.IO Hackathon, a team of students developed EEG Maze, a real-time Brain-Computer Interface (BCI) game that allows users to control movement in a 2D maze using EEG signals acquired with the Unicorn Hybrid Black EEG headset.
Built within just 24 hours, the project demonstrates how quickly developers can transform brain activity into interactive applications using the Unicorn Hybrid Black and the Unicorn Suite. The team combined real-time EEG acquisition, P300 signal detection, Python development, UDP communication, and game integration to create a working BCI application controlled entirely by user attention.
The system used visual stimuli to evoke P300 event-related potentials (ERPs), one of the most widely used paradigms in Brain-Computer Interface research and cognitive neuroscience. EEG signals were acquired with the Unicorn Hybrid Black, processed using the Unicorn Suite P300 tools, classified into directional commands, and transmitted to a Python-based maze game. The result was a real-time feedback loop where users could control game movement using only their brain activity.
One of the most impressive outcomes of this project is that the team was able to focus on BCI development, classifier optimization, latency reduction, and game interaction rather than spending valuable hackathon time on EEG hardware integration, signal acquisition, data quality issues, or artifact correction. Direct access to raw EEG data, real-time processing tools, UDP streaming, and built-in BCI functionality allowed the team to rapidly prototype a working application.
The project explored key topics in ERP research, P300 Brain-Computer Interfaces, real-time EEG processing, cognitive neuroscience, human-computer interaction, and neurotechnology development. Despite the complexity of integrating EEG classification with an interactive game environment, the team successfully achieved three-directional brain control and end-to-end latencies of approximately 300 milliseconds within a single hackathon.
Projects like EEG Maze demonstrate why the Unicorn Hybrid Black is widely used for Brain-Computer Interface research, ERP experiments, cognitive neuroscience, neurofeedback, Python-based EEG development, and rapid neurotechnology prototyping. By combining high-quality EEG acquisition, real-time data access, built-in BCI tools, and a complete software ecosystem, developers can move from brain signals to working applications in hours rather than weeks.
BR41N.IO Hackathon projects repeatedly show that researchers, students, startups, and developers can build real-time EEG applications, Brain-Computer Interfaces, games, assistive technologies, robotics systems, and machine learning solutions within 24 hours using the Unicorn Hybrid Black platform.
Read more: br41n.io/IEEE-SMC-2025
BR41N.IO Hackathon during IEEE SMC 2025 - Third Place winner - BCI Gaming
Can a user navigate a maze using nothing but brain signals? At the BR41N.IO Hackathon, a team of students developed EEG Maze, a real-time Brain-Computer Interface (BCI) game that allows users to control movement in a 2D maze using EEG signals acquired with the Unicorn Hybrid Black EEG headset.
Built within just 24 hours, the project demonstrates how quickly developers can transform brain activity into interactive applications using the Unicorn Hybrid Black and the Unicorn Suite. The team combined real-time EEG acquisition, P300 signal detection, Python development, UDP communication, and game integration to create a working BCI application controlled entirely by user attention.
The system used visual stimuli to evoke P300 event-related potentials (ERPs), one of the most widely used paradigms in Brain-Computer Interface research and cognitive neuroscience. EEG signals were acquired with the Unicorn Hybrid Black, processed using the Unicorn Suite P300 tools, classified into directional commands, and transmitted to a Python-based maze game. The result was a real-time feedback loop where users could control game movement using only their brain activity.
One of the most impressive outcomes of this project is that the team was able to focus on BCI development, classifier optimization, latency reduction, and game interaction rather than spending valuable hackathon time on EEG hardware integration, signal acquisition, data quality issues, or artifact correction. Direct access to raw EEG data, real-time processing tools, UDP streaming, and built-in BCI functionality allowed the team to rapidly prototype a working application.
The project explored key topics in ERP research, P300 Brain-Computer Interfaces, real-time EEG processing, cognitive neuroscience, human-computer interaction, and neurotechnology development. Despite the complexity of integrating EEG classification with an interactive game environment, the team successfully achieved three-directional brain control and end-to-end latencies of approximately 300 milliseconds within a single hackathon.
Projects like EEG Maze demonstrate why the Unicorn Hybrid Black is widely used for Brain-Computer Interface research, ERP experiments, cognitive neuroscience, neurofeedback, Python-based EEG development, and rapid neurotechnology prototyping. By combining high-quality EEG acquisition, real-time data access, built-in BCI tools, and a complete software ecosystem, developers can move from brain signals to working applications in hours rather than weeks.
BR41N.IO Hackathon projects repeatedly show that researchers, students, startups, and developers can build real-time EEG applications, Brain-Computer Interfaces, games, assistive technologies, robotics systems, and machine learning solutions within 24 hours using the Unicorn Hybrid Black platform.
Read more: br41n.io/IEEE-SMC-2025
