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Related Experiment Video

Updated: Aug 4, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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

Sajjad Karimi, Mohammad Bagher Shamsollahi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Latent Structure Influence Models (LSIMs) effectively model complex brain signals, outperforming traditional methods in capturing spatial-temporal dynamics and improving classification accuracy for EEG/ECoG data.

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    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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    Area of Science:

    • Computational neuroscience
    • Machine learning for biosignals
    • Time series analysis

    Background:

    • Brain signals (EEG/ECoG) are complex nonlinear, nonstationary time series.
    • Hidden Markov Models (HMMs) and Conditional HMMs (CHMMs) struggle with multi-channel data due to exponential parameter growth.
    • Latent Structure Influence Models (LSIMs) offer a solution by modeling interactions between hidden Markov chains.

    Purpose of the Study:

    • Extend HMM re-estimation algorithms to LSIMs for multi-channel brain signal analysis.
    • Prove the convergence properties of the LSIM re-estimation algorithm.
    • Evaluate LSIM performance in modeling and classification tasks using simulated and real EEG/ECoG data.

    Main Methods:

    • Developed a novel re-estimation algorithm for LSIMs, extending existing HMM techniques.
    • Proved algorithm convergence to stationary points using a new auxiliary function and established statistical theories.
    • Derived closed-form re-estimation formulas using marginal forward-backward parameters.
    • Validated convergence with simulated and real EEG/ECoG data.

    Main Results:

    • LSIMs demonstrate superior performance over HMMs and CHMMs in modeling embedded Lorenz systems and ECoG recordings (based on AIC/BIC).
    • LSIMs provide more reliable classification than HMMs, SVMs, and CHMMs on simulated data.
    • LSIM-based EEG biometric verification significantly improves Area Under Curve (AUC) by 6.8% and reduces AUC standard deviation.

    Conclusions:

    • LSIMs are a powerful tool for analyzing nonlinear, nonstationary multi-channel brain signals.
    • The extended re-estimation algorithm for LSIMs is theoretically sound and practically convergent.
    • LSIMs offer significant advantages in modeling, classification, and biometric applications using EEG/ECoG data.