Uploaded May 2025 | Updated September 2026, 1 day ago
How can you use Python to build real-time EEG experiments and Brain-Computer Interface (BCI) applications? In this presentation, Johannes Grünwald introduces g.Pype, g.tec’s Python framework for developing modular neuroscience and real-time biosignal processing applications.
The session explains why Python has become so important for neuroscience, from its extensive scientific ecosystem and machine-learning libraries to rapid prototyping, reproducible research, and custom experimental workflows.
Learn how g.Pype combines EEG acquisition, real-time signal processing, visualization, experimental control, data storage, and custom processing nodes in modular Python pipelines.
0:00 Introduction and motivation
0:59 Why Python for neuroscience
4:27 Requirements for research
10:18 The g.Pype solution
16:25 g.Pype architecture
18:18 g.Pype features and limitations
22:24 Availability and roadmap
25:00 Programming concepts
28:50 Live demonstration
38:52 Results and analysis
44:48 Q&A session
The live demonstrations show how to:
• Create node-based real-time signal processing pipelines
• Acquire and visualize EEG and biosignal data
• Apply filters and process signals in real time
• Record data to files
• Integrate keyboard events and experimental markers
• Connect EEG acquisition with experimental paradigms
• Analyze EEG responses using Python
• Build custom processing nodes and integrate third-party Python packages
• Develop reproducible neuroscience and BCI applications
The session also demonstrates a real EEG experiment using g.Nautilus, with visual stimuli and event markers followed by Python-based analysis of evoked brain responses. The experiment illustrates how acquisition, experimental control, signal processing, visualization, and analysis can be combined within a reproducible neuroscience workflow.
g.Pype is designed as a modular development platform for researchers and developers who want to create custom EEG, BCI, neurotechnology, machine-learning, and real-time biosignal processing applications in Python.
Explore g.Pype documentation:
https://gpype.gtec.at/
g.Pype on GitHub:
github.com/gtec-medical-engineering/gpype
g.Pype on PyPI:
pypi.org/project/gpype
More about g.tec medical engineering:
https://www.gtec.at/
How can you use Python to build real-time EEG experiments and Brain-Computer Interface (BCI) applications? In this presentation, Johannes Grünwald introduces g.Pype, g.tec’s Python framework for developing modular neuroscience and real-time biosignal processing applications.
The session explains why Python has become so important for neuroscience, from its extensive scientific ecosystem and machine-learning libraries to rapid prototyping, reproducible research, and custom experimental workflows.
Learn how g.Pype combines EEG acquisition, real-time signal processing, visualization, experimental control, data storage, and custom processing nodes in modular Python pipelines.
0:00 Introduction and motivation
0:59 Why Python for neuroscience
4:27 Requirements for research
10:18 The g.Pype solution
16:25 g.Pype architecture
18:18 g.Pype features and limitations
22:24 Availability and roadmap
25:00 Programming concepts
28:50 Live demonstration
38:52 Results and analysis
44:48 Q&A session
The live demonstrations show how to:
• Create node-based real-time signal processing pipelines
• Acquire and visualize EEG and biosignal data
• Apply filters and process signals in real time
• Record data to files
• Integrate keyboard events and experimental markers
• Connect EEG acquisition with experimental paradigms
• Analyze EEG responses using Python
• Build custom processing nodes and integrate third-party Python packages
• Develop reproducible neuroscience and BCI applications
The session also demonstrates a real EEG experiment using g.Nautilus, with visual stimuli and event markers followed by Python-based analysis of evoked brain responses. The experiment illustrates how acquisition, experimental control, signal processing, visualization, and analysis can be combined within a reproducible neuroscience workflow.
g.Pype is designed as a modular development platform for researchers and developers who want to create custom EEG, BCI, neurotechnology, machine-learning, and real-time biosignal processing applications in Python.
Explore g.Pype documentation:
https://gpype.gtec.at/
g.Pype on GitHub:
github.com/gtec-medical-engineering/gpype
g.Pype on PyPI:
pypi.org/project/gpype
More about g.tec medical engineering:
https://www.gtec.at/
