在不同的TMS-EEG数据集中识别对TMS和假刺激的EEG反应:一种机器学习方法
Ahmadreza Keihani1, Francesco L Donati2, Simone Russo3
1Department of Psychiatry, University of Pittsburgh, 3501 Forbes Avenue, Pittsburgh, PA 15213, USA.
NeuroImage
|February 13, 2026
概括
我们开发了一种机器学习模型,以区分EEG数据中的真实跨磁刺激 (TMS) 和假刺激的效果. 这个工具客观地验证了TMS唤起的潜力 (TEP),提高了神经生理学评估质量.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 与同步电脑电图 (TMS-EEG) 进行的跨磁刺激对于评估皮质神经元神经生理学至关重要.
- 然而,TMS引起的EEG电位 (TEP) 可能被非神经元人工物污染,需要进行质量控制.
- 需要客观的工具来区分真正的TMS神经元激活与假刺激效应.
研究的目的:
- 开发和验证一种客观的,自动化的方法,用于在EEG数据中区分主动TMS和假刺激反应.
- 评估机器学习模型在不同刺激和假条件下识别真实TEP的准确性.
- 在TMS-EEG研究中提供可靠的标准来验证TEP真实性.
主要方法:
- 使用了两个独立的TMS-EEG数据集,包括来自33名健康个体的27,590项试验.
- 使用双向长期短期记忆 (BiLSTM) 机器学习模型将EEG时间点分类为活跃的TMS或假的.
- 实施了新的比较,包括基线与后刺激EEG,以及活跃的TMS与假条件.
主要成果:
- BiLSTM模型在区分刺激后的TMS与基线EEG方面取得了中等到高的准确性,特别是在聚合试验中.
- 刺激后的TMS和刺激后的假条件之间的比较显示了中度至高准确度,除了有听觉点击噪音的情况下.
- 基线TMS和假条件之间的EEG比较产生了偶然水平的准确性,突出显示了后刺激分析的特异性.
结论:
- 活跃TMS后的TEP可靠地与使用BiLSTM模型的各种假刺激区分开来,即使在单个受试者水平上也很少有试验.
- 这种机器学习方法提供了客观的标准来支持TEP真实性,解决了TMS-EEG研究中的关键挑战.
- 这些发现可以帮助解决围绕TEP特征的争论,并改善TMS-EEG数据的解释.
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