School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.
Hubi Wuhan Public Security Bureau, No. 798, Wuluo Road, Wuhan City, Hubei 430070, China.
J Neurosci Methods. 2023 Sep 1;397:109936. doi: 10.1016/j.jneumeth.2023.109936. Epub 2023 Jul 29.
Closed-loop auditory stimulation is one of the well-known and emerging sensory stimulation techniques, which achieves the purpose of sleep regulation by driving the EEG slow oscillation (SO, <1 Hz) through auditory stimulation. The main challenge is to accurately identify the stimulation timing and provide feedback in real-time, which has high requirements on the response time and recognition accuracy of the closed-loop auditory stimulation system. To reduce the impact of systematic errors on the regulation results, most traditional closed-loop auditory stimulation systems try to identify a single feature to determine the timing of stimulus delivery and reduce the system feedback delay by simplifying the calculation. Unlike existing closed-loop regulation systems that identify specific brain features, this paper proposes a closed-loop auditory stimulation sleep regulation system deploying machine learning. The process is: through online sleep real-time automatic staging, tracking the sleep stage to provide feedback quickly, and continuously offering external auditory stimulation at a specific SO phase. This paper uses this system to conduct sleep auditory stimulation regulation experiments on ten subjects. The experimental results show that the sleep closed-loop regulation system proposed in this paper can achieve consistency (effective for almost all subjects in the experiment) and immediate (taking effect immediately after stimulation) modulation effects on SOs. More importantly, the proposed method is superior to existing advanced methods. Therefore, the system designed in this paper has great potential to be more reliable and flexible in sleep regulation.
闭环听觉刺激是一种众所周知的新兴感觉刺激技术，通过听觉刺激驱动脑电图慢波（SO，<1 Hz）来实现睡眠调节的目的。主要挑战是准确识别刺激时机并实时提供反馈，这对闭环听觉刺激系统的响应时间和识别精度有很高的要求。为了减少系统误差对调节结果的影响，大多数传统的闭环听觉刺激系统试图识别单一特征来确定刺激传递的时机，并通过简化计算来减少系统反馈延迟。与现有的识别特定大脑特征的闭环调节系统不同，本文提出了一种使用机器学习的闭环听觉刺激睡眠调节系统。该过程为：通过在线睡眠实时自动分期，快速跟踪睡眠阶段以提供反馈，并在特定 SO 阶段持续提供外部听觉刺激。本文使用该系统对 10 名受试者进行了睡眠听觉刺激调节实验。实验结果表明，本文提出的睡眠闭环调节系统可以对 SO 产生一致性（对实验中的几乎所有受试者都有效）和即时（刺激后立即生效）的调节效果。更重要的是，所提出的方法优于现有的先进方法。因此，本文设计的系统在睡眠调节方面具有更高的可靠性和灵活性的潜力。
