A recent advancement in the field of neuromodulation is to adapt stimulation parameters according to pre-specified biomarkers tracked in real-time. These markers comprise short and transient signal features, such as bursts of elevated band power. To capture these features, instantaneous measures of phase and/or amplitude are employed, which inform stimulation adjustment with high temporal specificity. For adaptive neuromodulation it is therefore necessary to precisely estimate a signal's phase and amplitude with minimum delay and in a causal way, i.e. without depending on future parts of the signal. Here we demonstrate a method that utilizes oscillation theory to estimate phase and amplitude in real-time and compare it to a recently proposed causal modification of the Hilbert transform. By simulating real-time processing of human LFP data, we show that our approach almost perfectly tracks offline phase and amplitude with minimum delay and is computationally highly efficient.

神经调节领域的最新进展是根据实时跟踪的预先指定的生物标志物调整刺激参数。这些标记包括短的和瞬态的信号特征，例如高频带功率的突发。为了捕捉这些特征，采用了相位和/或幅度的瞬时测量，以高时间特异性通知刺激调整。因此，对于自适应神经调制，必须以最小延迟和因果方式精确估计信号的相位和幅度，即不依赖于信号的未来部分。在这里，我们展示了一种利用振荡理论实时估计相位和幅度的方法，并将其与最近提出的希尔伯特变换的因果修正进行比较。通过模拟实时处理人类 LFP 数据，