Improving EEG Decoding for Stroke Rehabilitation with Unicorn Hybrid Black

Motor Imagery BCI & Machine Learning | Unicorn Hybrid Black | BR41N.IO Hackathon 2026 What can a team build in just 24 hours using real-time EEG and Brain-Computer Interface (BCI) technology? At the BR41N.IO BCI & Neurotechnology Hackathon, Team Lords of Confusion explored how machine learning and advanced EEG signal processing can improve Brain-Computer Interface performance for stroke rehabilitation. Their project focused on enhancing the accuracy of motor imagery decoding, a key component in BCI systems that are combined with Functional Electrical Stimulation (FES) to help stroke survivors regain arm and hand function. Brain-Computer Interfaces for neurorehabilitation work by decoding a patient's intention to move and translating that intention into therapeutic actions. Improving EEG classification accuracy is essential for making these systems more reliable and effective in real-world clinical applications. The team evaluated and optimized established BCI pipelines based on Common Spatial Patterns (CSP) and machine learning classifiers. They applied advanced EEG preprocessing techniques including band-pass filtering, bad channel interpolation, average re-referencing, automatic artifact rejection, and patient-specific time window optimization. The project compared CSP combined with Support Vector Machines (SVM) against traditional Linear Discriminant Analysis (LDA) approaches to identify the most effective neural decoding strategy. Their results demonstrated that machine learning-based classification with SVM consistently outperformed standard LDA methods across multiple datasets. The findings also highlighted the importance of personalized Brain-Computer Interface configurations, as optimal EEG decoding windows varied significantly between individuals. In addition, the project showed how artifact rejection and signal quality remain critical factors for successful real-time BCI deployment. This project demonstrates how EEG, Brain-Computer Interfaces, machine learning, neural decoding, motor imagery, cognitive neuroscience, and neurorehabilitation can be combined to advance the future of stroke recovery and assistive neurotechnology. More about Unicorn Hybrid Black: https://www.gtec.at/product/unicorn-h... More about BR41N.IO: https://www.gtec.at/hackathon/