Brain biomarkers of motor adaptation using phase synchronization

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cognitive Science

Background:

• Advanced brain monitoring tools are crucial for medical applications and assistive technologies like brain-computer interface (BCI) systems.
• Existing technologies lack reliable neural indicators to track brain dynamics during interaction with new tools or environments.
• Understanding cognitive-motor states is vital for developing effective neuroprosthetics and adaptive systems.

Purpose of the Study:

• To identify and investigate novel electroencephalography (EEG)-derived biomarkers for dynamic cognitive-motor states.
• To assess the feasibility of using phase synchronization measures as functional neural indicators during tool learning.
• To explore the correlation between neural activity changes and kinematic improvements in a learning context.

Main Methods:

• Utilized electroencephalography (EEG) to record brain activity during a tool-learning task.
• Applied phase synchronization measures, including coherence and phase locking value (PLV), to analyze EEG signals.
• Correlated changes in neural biomarkers with kinematic data reflecting task performance and adaptation.

Main Results:

• Demonstrated a linear decrease in phase synchronization during both movement planning and execution as subjects learned the new tool.
• Observed a significant correlation between reduced phase synchronization and enhanced kinematic performance.
• Identified non-invasive EEG biomarkers reflecting adaptive cognitive-motor states.

Conclusions:

• Phase synchronization measures derived from EEG show promise as functional neural indicators for cognitive-motor adaptation.
• These biomarkers can potentially be integrated into bioengineering and BCI systems for user-adaptive neuroprosthetics.
• The findings support the development of adaptive systems that facilitate co-adaptation between the user's brain and decoding algorithms.