The complete collection of functions and scripts for estimating and simulating time-varying Multivariate Autoregressive processes (tv-MVAR) by means of Kalman filtering and Self-Tuning Optimized Kalman filtering.
The toolbox includes:
- One demo (please refer to the file dynet_demo01.m for a brief tutorial)
- Four folders:
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'statespace'
- dynet_SSM_KF.m implements the Kalman filter for state-space modeling of physiological time series
- dynet_SSM_STOK.m implements the Self-Tuning Optimized Kalman filter
- dynet_SSM_siSTOK.m STOK filter with structural priors
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'connectivity'
- dynet_ar2pdc.m estimates the tv PDC from tv-AR coefficients
- dynet_connplot.m displays connectivity matrices (function of time and frequency) for each combination of signals
- dynet_parpsd.m estimates the AR coefficients in the frequency domain and the parametric power spectral density of the input signals
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'simulation'
- dynet_sim.m is the simulation framework for tv-MVAR generated surrogate time series
- review.m displays the 1) structural adjacency matrix, 2) the functional adjacency matrix, 3)surrogate time-series in the time domain,
- the power spectral density of surrogate time-series
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'utilities' contains all the invoked functions to let all the above listed functions/scripts to properly work
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