Thalamus: a real-time system for synchronized, closed-loop multimodal behavioral and electrophysiological data...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Medical Technology

Background:

• Precise, synchronized multimodal data capture is vital for understanding brain function.
• Advancing brain-computer interface (BCI) technology requires integrated neurosurgical data.
• Existing clinical environments lack unified systems for real-time neural and behavioral data recording.

Purpose of the Study:

• To introduce Thalamus, an open-source software platform for multimodal data capture in neurosurgical settings.
• To enable synchronous recording and real-time analysis of neural and behavioral data.
• To facilitate the development of closed-loop experiments and advanced analysis of motor functions.

Main Methods:

• Developed Thalamus, an open-source platform using a modular, node-based pipeline.
• Integrated common clinical sensors (pulse oximeters, inertial sensors, EMG, electrophysiology).
• Utilized a tiered Python and C++ architecture for flexibility and high-resolution sensor support.

Main Results:

• Thalamus enables synchronous recording of neural and behavioral data with sub-millisecond precision.
• The platform supports integration and concurrent analysis of diverse, high-resolution data streams.
• Validation experiments confirmed Thalamus's capability for robust data integration and synchronization.

Conclusions:

• Thalamus offers a powerful tool for enhancing neurosurgical research and clinical applications.
• Its compatibility with existing hardware facilitates adoption in clinical environments.
• The platform paves the way for data-driven neurological care and personalized treatment strategies.