  Electroencephalographic (EEG) monitoring of neural activity is widely used
for sleep disorder diagnostics and research. The standard of care is to
manually classify 30-second epochs of EEG time-domain traces into 5 discrete
sleep stages. Unfortunately, this scoring process is subjective and
time-consuming, and the defined stages do not capture the heterogeneous
landscape of healthy and clinical neural dynamics. This motivates the search
for a data-driven and principled way to identify the number and composition of
salient, reoccurring brain states present during sleep. To this end, we propose
a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), combined with
wide-sense stationary (WSS) time series spectral estimation to construct a
generative model for personalized subject sleep states. In addition, we employ
multitaper spectral estimation to further reduce the large variance of the
spectral estimates inherent to finite-length EEG measurements. By applying our
method to both simulated and human sleep data, we arrive at three main results:
1) a Bayesian nonparametric automated algorithm that recovers general temporal
dynamics of sleep, 2) identification of subject-specific "microstates" within
canonical sleep stages, and 3) discovery of stage-dependent sub-oscillations
with shared spectral signatures across subjects.
