Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Intracortical brain-computer interfaces (iBCIs) offer intuitive cursor control for individuals with tetraplegia.
• Long-term iBCI use is hindered by performance degradation due to shifts in neural data-decoder relationships.
• Identifying performance instability is crucial for optimizing iBCI recalibration and practical application.

Approach:

• Developed a novel method to quantify neural data instability without requiring labeled user intentions.
• Analyzed longitudinal data from two participants with tetraplegia using the BrainGate2 system over extended periods (142 and 28 days).
• Correlated the proposed instability measure with real-time changes in closed-loop cursor control performance.

Key Points:

• The proposed instability measure accurately reflects changes in iBCI performance.
• Demonstrated strong correlations between neural data instability and cursor performance (Pearson r = 0.93 and 0.72).
• The method allows for performance inference and recalibration timing determination using only neural activity.

Conclusions:

• This approach enables online estimation of iBCI performance based solely on recorded neural data.
• Provides a strategy for determining optimal recalibration points to maintain reliable iBCI function.
• Facilitates the development of more robust and practical long-term brain-computer interfaces for individuals with severe motor impairments.