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Tractable Maximum Likelihood Estimation for Latent Structure Influence Models with Applications to EEG & ECoG processing

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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.

History

Email Address of Submitting Author

sajjadkarimi91@gmail.com

ORCID of Submitting Author

0000-0002-4537-4001

Submitting Author's Institution

Sharif University of Technology

Submitting Author's Country

Iran