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闭环脑电相位触发经颅磁刺激中的延迟分析。

Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340744.

Abstract

The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves instantaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system's transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.

摘要

闭环 EEG 相位触发经颅磁刺激 (TMS) 的最新发展推进了基于当前脑状态的自适应神经调节的潜在应用。闭环 TMS 涉及 EEG 节律的即时获取、目标相位的定时预测和 TMS 的触发。然而,EEG 相位预测算法的准确性在很大程度上受到系统传输延迟的影响,相关工作很少考虑它们之间的关系。本文提出了一种延迟分析,将闭环 EEG 相位触发 TMS 系统的延迟作为验证相位预测算法的主要因素。使用真实 EEG 数据进行了仿真验证,比较了常用算法的性能。实验结果表明,总延迟对算法性能有显著影响,并且算法在不同延迟水平下的性能排名也不同。我们得出结论,延迟分析框架应广泛应用于闭环 EEG 相位触发 TMS 系统中相位预测算法的设计和验证。

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