Adaptive State-Space Multitaper Spectral Estimation

doi:10.1109/LSP.2022.3142670

Adaptive State-Space Multitaper Spectral Estimation

2022-01-01

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DOI：10.1109/LSP.2022.3142670

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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.

Keywords:

Kalman filters

Estimation

Time-frequency analysis

State-space methods

Time series analysis

Spectrogram

Signal to noise ratio

State-space model

spectral estimation

multi-taper method

adaptive estimation

Kalman filter

Adaptive State-Space Multitaper Spectral Estimation

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