Modelling state‐transition dynamics in resting‐state brain signals by the hidden Markov and Gaussian mixture models

@article{Ezaki2021ModellingSD,
  title={Modelling state‐transition dynamics in resting‐state brain signals by the hidden Markov and Gaussian mixture models},
  author={Takahiro Ezaki and Yu Himeno and Takamitsu Watanabe and Naoki Masuda},
  journal={The European Journal of Neuroscience},
  year={2021},
  volume={54},
  pages={5404 - 5416}
}
Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of… 
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