Background

Closed-loop phase-locked stimulation experiments are rare due to the unavailability of user-friendly algorithms and devices. Our goal is to provide an algorithm for the detection of oscillatory activity in local field potentials (LFPs) and phase prediction, which is user-friendly and robust to non-stationarities in LFPs of behaving animals.

New method

We propose an algorithm that only requires specification of the frequency range within which oscillatory episodes are tracked. Frequency-specific detection thresholds and filter parameters are adjusted automatically based on the short-time LFP power spectrum. Estimates of instantaneous frequency and instantaneous phase are used for phase extrapolation, taking advantage of Bayesian estimation. We used real LFP signals, recorded from a variety of different species and different brain areas, as well as artificial LFP signals with known properties to assess the detection and prediction performance of our algorithm and three previously published reference algorithms under various conditions.

Results and comparison with existing methods

Our algorithm, while significantly more user-friendly than previous approaches, provides a solid detection and prediction performance over a wide range of realistic conditions and, in many cases, has a longer prediction horizon than the reference algorithms. Due to its ability to adjust to changes in the signal, the algorithm is well-prepared to deal with non-stationarities in oscillation frequency, even in the presence of multiple oscillation components.

Conclusions

We have created a universal algorithm for oscillation detection and phase prediction, which performs well and is user-friendly at the same time, making closed-loop phase-locked stimulation experiments easier to accomplish.

中文翻译：

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一种用户友好的自适应闭环锁相刺激算法

背景

由于用户友好的算法和设备不可用，闭环锁相刺激实验很少见。我们的目标是提供一种用于检测局部场电位 (LFP) 中的振荡活动和相位预测的算法，该算法对行为动物的 LFP 中的非平稳性具有用户友好性和鲁棒性。

新方法

我们提出了一种算法，该算法只需要指定跟踪振荡事件的频率范围。根据短时 LFP 功率谱自动调整特定频率的检测阈值和滤波器参数。利用贝叶斯估计，瞬时频率和瞬时相位的估计用于相位外推。我们使用从各种不同物种和不同大脑区域记录的真实 LFP 信号，以及具有已知特性的人工 LFP 信号来评估我们的算法和三种先前发布的参考算法在各种条件下的检测和预测性能。

结果和与现有方法的比较

我们的算法虽然比以前的方法更加用户友好，但在广泛的现实条件下提供了可靠的检测和预测性能，并且在许多情况下，比参考算法具有更长的预测范围。由于其能够适应信号的变化，该算法已做好充分准备以处理振荡频率的非平稳性，即使存在多个振荡分量也是如此。

结论

我们创建了一种用于振荡检测和相位预测的通用算法，该算法性能良好，同时用户友好，使闭环锁相刺激实验更容易完成。