Uploaded October 2025 | Updated September 2026, 1 day ago
BR41N.IO Hackathon during IEEE SMC 2025 - First place winner - Data Analysis
Can machine learning decode what a person is seeing from brain activity alone? At the BR41N.IO Hackathon, Team NeuroPulse developed a real-time neurotechnology platform that combines brain signal analysis, machine learning, and visual scene classification to decode perception from neural recordings.
Built within just 24 hours, the project demonstrates how quickly teams can move from raw neural data to advanced AI-powered neuroscience applications. The team developed a complete pipeline for signal preprocessing, feature extraction, machine learning model development, real-time annotation, and web-based visualization, transforming complex brain recordings into meaningful predictions about visual perception.
The project explored how neural activity changes while viewing different naturalistic visual scenes. Using advanced signal processing and machine learning techniques, the team analyzed brain activity associated with multiple visual categories and compared several classification approaches, including Random Forests, Support Vector Machines, and Logistic Regression. Their best-performing models achieved classification accuracies of up to 83% across multiple stimulus categories, demonstrating the potential of machine learning for neural decoding and cognitive neuroscience research.
Beyond offline analysis, the team developed a real-time annotation system that combines computer vision, brain signal analysis, and interactive visualizations. They also created a comprehensive web application that allows users to explore neural activity, feature extraction methods, machine learning performance, brain-region activation patterns, and real-time classification results within a single interface.
Projects like NeuroPulse highlight key areas of modern neuroscience research, including machine learning, brain decoding, cognitive neuroscience, neural signal processing, artificial intelligence, human-computer interaction, and real-time neuroscience. The project demonstrates how rapidly interdisciplinary teams can build advanced neurotechnology solutions when they can focus on algorithms, analysis, and innovation rather than infrastructure and data acquisition challenges.
BR41N.IO Hackathon projects repeatedly demonstrate that researchers, students, startups, and developers can build sophisticated neurotechnology applications, machine learning pipelines, Brain-Computer Interfaces, real-time neuroscience tools, and AI-driven brain analysis platforms within just 24 hours.
More about Unicorn Hybrid Black: https://www.gtec.at/product/unicorn-hybrid-black-bci-platform
More about BR41N.IO: https://www.gtec.at/hackathon/
BR41N.IO Hackathon during IEEE SMC 2025 - First place winner - Data Analysis
Can machine learning decode what a person is seeing from brain activity alone? At the BR41N.IO Hackathon, Team NeuroPulse developed a real-time neurotechnology platform that combines brain signal analysis, machine learning, and visual scene classification to decode perception from neural recordings.
Built within just 24 hours, the project demonstrates how quickly teams can move from raw neural data to advanced AI-powered neuroscience applications. The team developed a complete pipeline for signal preprocessing, feature extraction, machine learning model development, real-time annotation, and web-based visualization, transforming complex brain recordings into meaningful predictions about visual perception.
The project explored how neural activity changes while viewing different naturalistic visual scenes. Using advanced signal processing and machine learning techniques, the team analyzed brain activity associated with multiple visual categories and compared several classification approaches, including Random Forests, Support Vector Machines, and Logistic Regression. Their best-performing models achieved classification accuracies of up to 83% across multiple stimulus categories, demonstrating the potential of machine learning for neural decoding and cognitive neuroscience research.
Beyond offline analysis, the team developed a real-time annotation system that combines computer vision, brain signal analysis, and interactive visualizations. They also created a comprehensive web application that allows users to explore neural activity, feature extraction methods, machine learning performance, brain-region activation patterns, and real-time classification results within a single interface.
Projects like NeuroPulse highlight key areas of modern neuroscience research, including machine learning, brain decoding, cognitive neuroscience, neural signal processing, artificial intelligence, human-computer interaction, and real-time neuroscience. The project demonstrates how rapidly interdisciplinary teams can build advanced neurotechnology solutions when they can focus on algorithms, analysis, and innovation rather than infrastructure and data acquisition challenges.
BR41N.IO Hackathon projects repeatedly demonstrate that researchers, students, startups, and developers can build sophisticated neurotechnology applications, machine learning pipelines, Brain-Computer Interfaces, real-time neuroscience tools, and AI-driven brain analysis platforms within just 24 hours.
More about Unicorn Hybrid Black: https://www.gtec.at/product/unicorn-hybrid-black-bci-platform
More about BR41N.IO: https://www.gtec.at/hackathon/
