Meta-AlignNN: A meta-learning framework for stable brain-computer interface performance across subjects, time, and tasks

Abstract

Practical application of brain-computer interfaces (BCIs) requires stable mapping between neuronal activity and behavior through various behavioral contexts and for different individuals. Due to neural activity instability, BCIs require frequent recalibration to maintain robust performance. Early approaches to addressing BCI stability issues mainly focused on tackling the challenge of neural activity changes over time. However, future BCI applications involve diversified scenarios and subjects, requiring solutions that address neural variability across time, subjects, and tasks. This study proposes a meta-learning-based algorithm to achieve BCI stability, named "Meta-AlignNN." By capitalizing on the consistency of neural population dynamics, it provides a unified solution for maintaining BCI stability, robustness, and scalability across subjects, time, and tasks. Tested over two years on four tasks with three monkeys, as well as on public datasets, the approach has achieved significantly excellent performance in both offline decoding and real-time brain-control, outperforming existing methods. Our findings provides a foundation for meeting the clinical demands of longterm, efficient, and stable usability across patients and tasks, offering a compelling solution for practical BCI applications.