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Tractable Maximum Likelihood Estimation for Latent Structure Influence Models with Applications to EEG & ECoG processing
  • Sajjad Karimi ,
  • Mohammad Bagher Shamsollahi
Sajjad Karimi
Sharif University of Technology

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Mohammad Bagher Shamsollahi
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Abstract

Brain signals are nonlinear and nonstationary time series, which provide information about spatiotemporal patterns of electrical activity in the brain. CHMMs are suitable tools for modeling multi-channel time-series dependent on both time and space, but state-space parameters grow exponentially with the number of channels. To cope with this limitation, we consider the influence model as the interaction of hidden Markov chains called Latent Structure Influence Models (LSIMs). LSIMs are capable of detecting nonlinearity and nonstationarity, making them well suited for multi-channel brain signals.
Aug 2023Published in IEEE Transactions on Pattern Analysis and Machine Intelligence volume 45 issue 8 on pages 10466-10477. 10.1109/TPAMI.2023.3244130