Personalizing brain stimulation: continual learning for sleep spindle detection.

Abstract

Personalized closed-loop brain stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occur during non rapid eye movement sleep that are involved in memory consolidation-presents an exciting frontier for studying memory functions; however, this endeavor is challenged by the spindles' fleeting nature, inter-individual variability, and the necessity of real-time detection. Methods: This paper introduces an approach to tackle these challenges, centered around a novel continual learning framework. Using a pre-trained model capable of both online classification of sleep stages and spindle detection, we implement an algorithm that refines spindle detection, tailoring it to the individual throughout one or more nights without manual intervention. Results: Our methodology achieves accurate, subject-specific targeting of sleep spindles and enables advanced closed-loop stimulation studies. Conclusion: While fine-tuning alone offers minimal benefits for single nights, our approach combining weight averaging demonstrates significant improvement over multiple nights, effectively mitigating catastrophic forgetting. Significance: This advancement represents a crucial step towards personalized closed-loop brain stimulation, potentially leading to a deeper understanding of sleep spindle functions and their role in memory consolidation. It holds the promise of deepening our understanding of sleep spindles' role in memory consolidation for cognitive neuroscience research and therapeutic applications.