  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.
