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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

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

Published on: October 24, 2012

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

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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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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Published on: October 24, 2012

Cortical Source Analysis of High-Density EEG Recordings in Children

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Published on: June 30, 2014

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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.