Switching state-space modeling of neural signal dynamics
Fig 8
Automatic segmentation of sleep spindles using the VI-I EM method.
Three 30 s EEG recordings of NREM-2 sleep were segmented with the variational EM learning method with interpolated densities (VI-I EM) to identify spindles in an unsupervised manner. In each of the (a)-(c) recordings, the three sub-panels visualize spindle activity using a spectrogram, the original time trace, and the estimated real part of spindle waveform with 95% confidence intervals from posterior covariances. The learned spindle center frequencies are displayed in blue in parentheses. The estimated posterior model probabilities for the candidate model with both slow oscillations and spindles are overlaid on the time traces in blue lines. Shaded pink bars indicate spindles identified by a wavelet-based method for comparison. S = Spindle; NS = No Spindle.