Switching state-space modeling of neural signal dynamics
Fig 7
Real-world example: Comparisons of switching inference algorithms for sleep spindle detection.
(a) In the top three panels, the spindle activity is visualized using a spectrogram, the original time trace, and after being bandpass filtered within 10 Hz–16 Hz. The fourth panel shows the posterior model probabilities of st = 1 estimated by variational EM learning with interpolated densities (VI-I EM, blue color). The margins of spindle events identified by VI-I EM are also marked with vertical dashed lines. The last two panels display the estimated real (blue) and imaginary (magenta) spindle waveforms with 95% confidence intervals from posterior covariances. The learned spindle center frequency is displayed in blue in parentheses. (b) Estimated posterior model probabilities of st = 1 by other algorithms in comparison. A model probability closer to 1 suggests the presence of spindles (S = Spindle), while being closer to 0 indicates no spindle (NS = No Spindle). The 0.5 model probability is marked with gray horizontal dashed lines. VI-A EM = variational EM learning with deterministic annealing (orange color); Static = static switching method; IMM = interacting multiple models method; S&S 1991 = the Gaussian merging method in [34].