g.tec Brain-Computer Interface

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].

Technology Platform

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].

Technology Platform

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 research

Comparison 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: