Short-time Fourier transform (STFT) is the most common window-based approach for analyzing the spectrotemporal dynamics of time series. To mitigate the effects of high variance on the spectral estimates due to finite-length, independent STFT windows, state-space multitaper (SSMT) method used a state-space framework to introduce dependency among the spectral estimates. However, the assumed time-invariance of the state-space parameters makes the spectral dynamics difficult to capture when the time series is highly nonstationary. We propose an adaptive SSMT (ASSMT) method as a time-varying extension of SSMT. ASSMT tracks highly nonstationary dynamics by adaptively updating the state parameters and Kalman gains using a heuristic, computationally efficient exponential smoothing technique. In analyses of simulated data and real human electroencephalogram (EEG) recordings, ASSMT showed improved denoising and smoothing properties relative to standard multitaper and SSMT approaches.

翻译：短时 Fourier变换(STFT)是分析时序光谱动态的最常用的基于窗口的方法。为了减轻由于短长、独立的STFT窗口而导致的光谱估计差异很大的影响,州-空间多塔(SSMT)方法使用州-空间框架在光谱估计中引入依赖性。然而,假定国家-空间参数的时间变化使得当时间序列高度非静止时难以捕捉光光光谱动态。我们提议采用适应性SSMT(ASSMT)方法作为SSMT(ASSMT)的时间分配延伸。ASSMT(ASSMT)跟踪高度非静止动态,利用超常、计算高效的指数平滑技术适应性更新国家参数和Kalman收益。在分析模拟数据和真实的人类电光学记录时,ASSMT(EG)显示相对于标准的多塔和SSMT(SSMT)方法,已改进了光学和光滑性特性。