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http://dx.doi.org/10.7471/ikeee.2019.23.3.805

Low Noise Time-Frequency Analysis Algorithm for Real-Time Spectral Estimation  

Kim, Yeon-Su (Dept. of Electronics and Control Engineering, Hanbat National University)
Park, Beom-Su (Dept. of Electronics and Control Engineering, Hanbat National University)
Kim, Seong-Eun (Dept. of Electronics and Control Engineering, Hanbat National University)
Publication Information
Journal of IKEEE / v.23, no.3, 2019 , pp. 805-810 More about this Journal
Abstract
We present a time-frequency analysis algorithm based on the multitaper method and the state-space frameworks. In general, time-frequency representations have a trade-off between the time duration and the spectral bandwidth by the uncertainty principle. To optimize the trade-off problems, the short-time Fourier transform and wavelet based algorithms have been developed. Alternatively, the authors proposed the state-space frameworks based on the multitaper method in the previous work. In this paper, we develop a real-time algorithm to estimate variances and spectrum using the state-space framework. We test our algorithm in spectral analysis of simulated data.
Keywords
Bayesian parameter estimation; EEG; Kalman filter; multitaepr; spectrum estimation;
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