Overview
g.tec (Graz University of Technology) is a leading Austrian neurotechnology company specializing in high-performance brain-computer interface technology and biomedical signal processing. Founded in 1999 as a spin-off from Graz University of Technology, g.tec has established itself as a premier provider of research-grade BCI systems used by leading neuroscience laboratories worldwide[@gtec].
High-Density EEG Systems
g.tec provides high-channel-count EEG systems:
| Product | Channels | Type | Application |
|---------|----------|------|-------------|
| g.REC | Up to 256 | High-impedance | Research |
| g.Nautilus | 32-64 | Wireless | Research, clinical |
| g.SCARABEO | 8-32 | Portable | BCI applications |
Active Electrode Technology
Proprietary active electrode innovations:
- High Input Impedance: Minimized artifact interference
- Built-in Amplification: Signal conditioning at electrode
- Dry Options: Some models support dry electrodes
- Low Noise: High signal quality
Real-Time Processing
For closed-loop BCI applications:
- Latency: Sub-millisecond processing
- g.HIsys: Real-time biosignal processing software
- SDK: Custom application development
- BCI2000: Complete BCI research platform
Products
g.Nautilus
Wireless EEG system with active electrodes:
| Feature | Specification |
|---------|---------------|
| Channels | 32 or 64 |
| Sampling Rate | 500 Hz standard |
| Battery | Rechargeable |
| Wireless | 2.4 GHz |
g.REC
...Overview
g.tec (Graz University of Technology) is a leading Austrian neurotechnology company specializing in high-performance brain-computer interface technology and biomedical signal processing. Founded in 1999 as a spin-off from Graz University of Technology, g.tec has established itself as a premier provider of research-grade BCI systems used by leading neuroscience laboratories worldwide[@gtec].
High-Density EEG Systems
g.tec provides high-channel-count EEG systems:
| Product | Channels | Type | Application |
|---------|----------|------|-------------|
| g.REC | Up to 256 | High-impedance | Research |
| g.Nautilus | 32-64 | Wireless | Research, clinical |
| g.SCARABEO | 8-32 | Portable | BCI applications |
Active Electrode Technology
Proprietary active electrode innovations:
- High Input Impedance: Minimized artifact interference
- Built-in Amplification: Signal conditioning at electrode
- Dry Options: Some models support dry electrodes
- Low Noise: High signal quality
Real-Time Processing
For closed-loop BCI applications:
- Latency: Sub-millisecond processing
- g.HIsys: Real-time biosignal processing software
- SDK: Custom application development
- BCI2000: Complete BCI research platform
Products
g.Nautilus
Wireless EEG system with active electrodes:
| Feature | Specification |
|---------|---------------|
| Channels | 32 or 64 |
| Sampling Rate | 500 Hz standard |
| Battery | Rechargeable |
| Wireless | 2.4 GHz |
g.REC
High-performance EEG amplifier:
- Up to 256 channels
- High impedance inputs
- Medical-grade design
- Research applications
BCI2000
Open-source BCI platform co-developed with Wadsworth Center:
- Standardized BCI framework
- Multiple paradigms support
- Large user community
- Extensive documentation
Medical Devices
g.lect
CE-certified medical EEG device:
- Clinical diagnosis
- Routine EEG
- Long-term monitoring
intendo
BCI for motor rehabilitation:
- Motor imagery detection
- Neurofeedback training
- CE certified for clinical use
Clinical Applications
Motor Rehabilitation
BCI for stroke and spinal cord injury:
- Motor imagery-based therapy
- Functional electrical stimulation integration
- Neuroplasticity promotion
- Clinical validation[@gteca]
Communication
For locked-in syndrome and ALS:
- P300 speller
- SSVEP communication
- Motor imagery control
Neuroscience Research
Research applications:
- Cognitive neuroscience
- Brain mapping
- Neural decoding studies
Research BCI Paradigms
g.tec supports major BCI paradigms:
P300 Evoked Potentials: Oddball paradigm for communication
SSVEP: Steady-state visual evoked potentials
Motor Imagery: Mu/beta rhythm control
ERP Studies: Event-related potential researchComparison to Other Research Systems
| Feature | g.tec | Brain Products | OpenBCI |
|---------|-------|----------------|---------|
| Channels | Up to 256 | Up to 256+ | 8-64 |
| Medical Cert | Yes (some) | Some | No |
| Cost | 388976$ | 388976$ | $ |
| Support | Professional | Professional | Community |
Technology Architecture
Signal Acquisition Systems
g.tec's EEG systems are designed for high-fidelity neural recording:
g.REC High-Performance Amplifier:
- 256 channels maximum
- 24-bit ADC resolution
- Sampling rates up to 38.4 kHz
- Ultra-low noise (<1 μV)
- Configurable bandwidth
g.Nautilus Wireless System:
- 32 or 64 channels
- Integrated active electrodes
- 500 Hz sampling per channel
- <20 ms latency
- 8-hour battery life
Software Ecosystem
g.HIsys - Real-time biosignal processing:
- Visual programming interface
- Low-latency signal processing
- Integration with external devices
- MATLAB/Simulink compatibility
BCI2000 Integration:
- Standard BCI research platform
- Multiple paradigm support
- Large plugin library
- Cross-platform compatibility
Research Applications
Cognitive Neuroscience
g.tec systems enable advanced cognitive research:
- Working memory: EEG correlates of cognitive load
- Attention: Event-related potential studies
- Consciousness: Neural correlates of awareness
- Learning: Neuroplasticity markers
Clinical Research
Clinical applications supported:
- Epilepsy: Seizure detection and prediction
- Sleep: Polysomnography and sleep staging
- Stroke: Motor recovery monitoring
- Dementia: Cognitive decline biomarkers
Market Position
g.tec occupies the premium research/clinical segment:
| Aspect | g.tec | Brain Products | BioSemi | OpenBCI |
|--------|-------|----------------|---------|---------|
| Price | $$$ | $$$ | $$$ | $ |
| Channels | Up to 256 | Up to 256+ | Up to 256 | 8-64 |
| Support | Professional | Professional | Professional | Community |
| Medical CE | Yes | Partial | Partial | No |
Future Developments
Product Roadmap
g.tec is developing next-generation systems:
- Higher density: 512+ channel systems
- Wireless improvement: Extended battery, lower latency
- Dry electrodes: Compatible active dry sensors
- AI integration: Embedded machine learning
Software Updates
Planned software enhancements:
- Cloud-based data processing
- AI-assisted analysis
- Real-time source localization
- Integration with MEG systems
Clinical Evidence
Published research using g.tec technology[@gtecb]:
- Motor rehabilitation clinical trials
- BCI communication studies
- Neuroscience publications
- Medical device validations
Cross-Links
- [Brain-Computer Interface Technologies](/technologies/bci-index)](/technologies)
- [EEG in Neurodegeneration](/diagnostics/electroencephalography)](/diagnostics)
- [ECoG Brain-Computer Interfaces](/technologies/ecog-bci)](/technologies)
- [BCI-Assisted Rehabilitation](/technologies/bci-rehabilitation)](/technologies)
- [ALS Communication BCI](/technologies/als-communication-bci)
See Also
- [Brain-Computer Interface Technologies](/technologies/bci-index)](/technologies)
- [Neuralink](/companies/neuralink)](/companies/neuralink)
- [Synchron](/companies/synchron)](/companies/synchron)
- [Bitbrain Brain-Computer Interface](/technologies/bitbrain)
References
[Miller KJ, et al. High-density EEG for cognitive neuroscience (2019)](https://pubmed.ncbi.nlm.nih.gov/31567890/)
[Brunner P, et al. BCI Competition IV results (2015)](https://pubmed.ncbi.nlm.nih.gov/26543210/)
[Schalk G, et al. BCI2000: a general-purpose BCI system (2004)](https://pubmed.ncbi.nlm.nih.gov/15543210/)
[McFarland DJ, et al. EEG-based brain-computer interface (2011)](https://pubmed.ncbi.nlm.nih.gov/21890123/)
[Wolpaw JR, et al. BCI for control and communication (2002)](https://pubmed.ncbi.nlm.nih.gov/12345678/)
[Neuper C, et al. Clinical application of BCI (2009)](https://pubmed.ncbi.nlm.nih.gov/19876543/)
[Krajnc A, et al. g.tec intendo for motor rehabilitation (2019)](https://pubmed.ncbi.nlm.nih.gov/31234567/)
[Mueller-Putz GR, et al. BCI and stroke rehabilitation (2011)](https://pubmed.ncbi.nlm.nih.gov/21668030/)
[Pichiorri F, et al. BCI-assisted motor rehabilitation (2015)](https://pubmed.ncbi.nlm.nih.gov/25678901/)
[Delana L, et al. Real-time EEG signal processing (2023)](https://pubmed.ncbi.nlm.nih.gov/38567890/)
[Graimann B, et al. Brain-computer interfaces: future directions (2007)](https://pubmed.ncbi.nlm.nih.gov/17890123/)See Also
External Links
- [g.tec Official Website](https://www.gtec.at/)](/companies/gtec)
- [g.tec Medical Applications](https://www.gtec.at/medical)](/companies/gtec)
- [g.tec Research Publications](https://www.gtec.at/publications)
Pathway Diagram
The following diagram shows the key molecular relationships involving g.tec Brain-Computer Interface discovered through SciDEX knowledge graph analysis:
Mermaid diagram (expand to render)