OSCAR LIVE Animal EEG: Freely Moving Alpaca with Unicorn BCI Core-8 @gtecmedicalengineering

Uploaded September 2025 | Updated September 2026, 1 day ago

Can you record EEG from an alpaca while it moves freely? This demonstration shows OSCAR LIVE during a wireless animal EEG recording from Merlin the alpaca using the Unicorn BCI Core-8 with Gold Cup Electrodes.

The compact Unicorn BCI Core-8 is mounted directly on Merlin’s halter and connected to 4 Gold Cup EEG Electrodes positioned for the recording. This setup allows EEG signals to be acquired while Merlin behaves and moves naturally, demonstrating an approach to animal EEG outside traditional laboratory environments.

Natural movement can introduce substantial artifacts into EEG recordings. OSCAR LIVE corrects movement-related EEG artifacts in real time, allowing researchers to see the effect of movement on the EEG while the signals are being processed.

The Unicorn BCI Core-8 provides 24-bit EEG acquisition, wireless data transmission, flexible electrode placement and real-time data streaming, making the system suitable for experimental setups where compact hardware and freedom of movement are important.

Combined with OSCAR LIVE, the setup is particularly relevant for research involving animal EEG, veterinary neuroscience, animal cognition, animal welfare, behavioral neuroscience, mobile EEG, wearable EEG and real-world electrophysiology.

OSCAR LIVE is included in g.HIsys Professional, integrating real-time EEG artifact correction into g.tec’s biosignal acquisition and real-time processing environment. For offline EEG analysis and artifact correction, OSCAR PRO is included in g.BSanalyze Professional.

More about Unicorn BCI Core-8:
https://www.gtec.at/product/unicorn-bci-core-8/

More about g.HIsys Professional:
https://www.gtec.at/product/ghisys-biosignal-acquisition-analysis/

More about g.BSanalyze Professional:
https://www.gtec.at/product/g-bsanalyze/

OSCAR LIVE Animal EEG: Freely Moving Alpaca with Unicorn BCI Core-8

Transmitting EEG data from the Unicorn BCI-Core-8 to the Samsung Galaxy

Before/After recoveriX - stroke - 9HPT - ST8

$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/$

Before/After recoveriX - stroke - BBT - ST10

Before/After recoveriX - Spinal Cord Injury - TUG - ST44

Reconstructed 3D Brain Mapping and Real-Time Motor Imagery Classification via g.tec g.BSanalyze

Before/After recoveriX - MS - T25FW - MS2

High-Frequency Oscillations and 3D Mapping in Grid and Stereo-EEG Recordings

EEG-Controlled Robot Navigation in Dynamic Environments with Unicorn Hybrid Black

Before/After recoveriX - Parkinsons - 10MWT - ST41