AI-READY EEG & BCI SYSTEMS
Real-time, research-grade EEG brain–computer interfaces that enable Neuro-AI and machine-learning systems to learn directly from real human brain signals.
WHAT IS “BCI FOR AI”?
Brain-computer interfaces (BCIs) for AI use EEG signals as a direct, high-bandwidth input for machine-learning systems. Instead of relying only on behavioral data or labels, AI models can learn from real neural activity, enabling human-in-the-loop, adaptive, and multimodal AI systems.
g.tec medical engineering provides the hardware and software infrastructure required to acquire, stream, and process EEG data for AI research and applications.
| g.Nautilus PRO FLEXIBLE (flagship for real-time, research-grade Neuro-AI) |
| g.USBamp (gold standard for precise timing, synchronization, and offline AI training) |
| Unicorn Hybrid Black (fast prototyping, AI education, startups, demos) |
| g.tec Suite 2024 (APIs, Python, pipelines, real-time) |
| human-in-the-loop machine learning |
| real-time cognitive state detection (attention, workload, error signals) |
| adaptive AI systems reacting to brain activity |
| multimodal AI (EEG + vision, audio, behavioral data) |
g.NAUTILUS PRO FLEXIBLE WEARABLE EEG FOR REAL-TIME NEURO-AI
g.Nautilus PRO is a wearable, wireless, research-grade EEG system designed for real-time brain data acquisition, making it ideally suited for AI experiments, adaptive algorithms, and closed-loop systems by providing precise real-time EEG streaming, supporting both mobile and lab-based experimental setups, and enabling online decoding and closed-loop learning as a flagship Neuro-AI sensor.
WHY IT MATTERS FOR AI
- Human-in-the-loop machine learning
- Real-time cognitive state detection (attention, workload, error signals)
- Adaptive AI systems reacting to brain activity
- Multimodal AI (EEG + vision, audio, behavioral data)
UNICORN BCI CORE-8 FOR AI PROTOTYPING & MULTIMODAL EXPERIMENTS
The Unicorn BCI Core-8 is a compact, wireless, research-grade EEG and BCI system designed for AI-driven experiments, rapid prototyping, and flexible data acquisition, providing up to 8 channels at 24-bit resolution for high-quality EEG data and, when used with the g.Pype Python SDK, enables direct access to real-time EEG data for training machine-learning models, rapid Neuro-AI prototyping and proof-of-concept development, multimodal AI experiments combining EEG with vision, VR, audio, or behavioral data, and human-in-the-loop adaptive AI systems driven by neural feedback.
WHY IT MATTERS FOR AI
- Wireless, real-time EEG streaming suitable for online and offline AI workflows
- Direct Python integration for signal processing, feature extraction, and model training
- Flexible electrode options enabling experiments in humans and animals
- Ideal for AI education, startups, and applied Neuro-AI research
UNICORN HYBRID BLACK FOR NEURO-AI & EDUCATION
Unicorn Hybrid Black is a compact EEG headset designed for fast prototyping in Neuro-AI and AI education, enabling rapid experimentation, proof-of-concept development, student projects, hackathons, and startup-level Neuro-AI applications, with direct access to EEG data via a Python API, allowing seamless integration into machine-learning workflows and fast validation of AI ideas.
WHY IT MATTERS FOR AI
- Quick setup
- Direct access to EEG data
- Easy integration into Python-based ML pipelines
g.tec Suite 2024 - SOFTWARE & AI INTEGRATION
g.tec systems integrate into modern AI workflows through g.tec Suite 2024, which provides real-time signal processing, Python-compatible interfaces, and online and offline EEG processing pipelines, supporting closed-loop and adaptive systems and enabling seamless integration with machine learning, deep learning, and multimodal AI frameworks.
WHY IT MATTERS FOR AI
- Real-time human-in-the-loop AI by providing continuous neural feedback instead of delayed behavioral labels
- Adaptive and closed-loop AI systems that adjust model behavior based on cognitive state and brain activity
- High-quality, physiologically grounded data for training and validating machine-learning and deep-learning models
- Multimodal AI integration, combining EEG with vision, audio, text, or behavioral data streams
- AI interpretability and safety by validating model decisions directly at the neural level
BRAIN-COMPUTER INTERFACES FOR AI
Explore how brain-computer interfaces and EEG data enable human-in-the-loop AI, adaptive machine learning, and Neuro-AI research, with g.tec systems supporting real-world BCI experiments.
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