Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation.
Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation.
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DOI：
10.1088/1741-2552/ab9dba
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发表时间：
2020-07-13
影响因子：
4
通讯作者：
Opitz A
中科院分区：
文献类型：
--
作者：
Shirinpour S;Alekseichuk I;Mantell K;Opitz A
基于特定EEG相位的经颅磁刺激（TMS）的实时方法是更精确的神经调节干预的有前途的途径。然而，可靠地提取感兴趣的频带中的EEG相位以通知TMS的最佳方法仍有待确定。在这里，我们实现了一个新的实时相位检测方法，闭环脑电经颅磁刺激鲁棒相位提取。我们比较这个算法与国家的最先进的方法，并评估其性能在硅片和实验。我们提出了一种新的鲁棒算法（教育的时间预测）提供实时脑电相位特定的刺激的基础上短的预先记录的脑电训练数据。该方法从训练周期估计峰间周期，并应用偏差校正来预测未来的峰。我们使用预先记录的静息脑电信号数据和实时实验，将ETP算法的准确性和计算速度与两种现有方法（基于傅立叶的、自回归预测）进行比较。我们发现，无论是在计算机模拟还是在体内，教育时间预测都比基于傅里叶或自回归的方法具有更高的准确性，同时计算效率更高。此外，我们记录了EEG信噪比（SNR）对所有算法的算法精度的依赖性。我们的研究结果为实时EEG-TMS技术开发和实验设计提供了重要的见解。由于其鲁棒性和计算效率，我们的方法可以在实验研究或临床应用中找到广泛的用途。通过开放共享所有三种方法的代码，我们使TMS-EEG实时算法能够广泛地进入社区。
Real-time approaches for Transcranial Magnetic Stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust phase extraction. We compare this algorithm with state-of-the-art methods and evaluate its performance both in silico and experimentally. We propose a new robust algorithm (Educated Temporal Prediction) for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data. This method estimates the interpeak period from a training period and applies a bias correction to predict future peaks. We compare the accuracy and computation speed of the ETP algorithm with two existing methods (Fourier based, Autoregressive Prediction) using prerecorded resting EEG data and real-time experiments. We found that Educated Temporal Prediction performs with higher accuracy than Fourier-based or Autoregressive methods both in silico and in vivo while being computationally more efficient. Further, we document the dependency of the EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms. Our results give important insights for real-time EEG-TMS technical development as well as experimental design. Due to its robustness and computational efficiency, our method can find broad use in experimental research or clinical applications. Through open sharing of code for all three methods, we enable broad access of TMS-EEG real-time algorithms to the community.
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影响因子：
7.7
作者：
Madsen, Kristoffer Hougaard;Karabanov, Anke Ninija;Siebner, Hartwig Roman
通讯作者：
Siebner, Hartwig Roman
联合TMS和EEG的方法。
影响因子：
2.7
作者：
Ilmoniemi, Risto J.;Kicic, Dubravko
通讯作者：
Kicic, Dubravko
影响因子：
10.6
作者：
O'Reardon, John P.;Solvason, H. Brent;Sackeim, Harold A.
通讯作者：
Sackeim, Harold A.
细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
2.5
作者：
Keil, Julian;Timm, Jana;Schoenwiesner, Marc
通讯作者：
Schoenwiesner, Marc