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Related Experiment Video

Updated: Sep 11, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Exploring an EM-algorithm for banded regression in computational neuroscience.

Søren A Fuglsang^1,2, Kristoffer H Madsen^1,3, Oula Puonti^1,4

¹Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces a new regression framework for computational neuroscience, enabling differential shrinkage of predictor groups. The method uses an expectation-maximization algorithm for tuning hyperparameters, applicable to fMRI and EEG data analysis.

Keywords:

EEG decoding encoding fMRI regularization

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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Area of Science:

Computational Neuroscience
Machine Learning
Statistical Modeling

Background:

Regression analysis is crucial for linking brain activity to stimuli or tasks.
Linear models with grouped predictors are common in encoding and decoding analyses.
Controlling the shrinkage of different predictor groups is often necessary.

Purpose of the Study:

To present a flexible framework for fitting regression models with differential group shrinkage.
To develop an expectation-maximization algorithm for hyperparameter tuning in these models.
To demonstrate the model's utility in neuroimaging data analysis.

Main Methods:

Developed a novel regression framework allowing differential shrinkage of predictor weight groups.
Implemented an expectation-maximization algorithm for hyperparameter optimization.
Validated the model using simulated data, BOLD fMRI encoding, and EEG decoding analyses.

Main Results:

The proposed framework allows for straightforward definition and estimation of models with differential group shrinkage.
The expectation-maximization algorithm effectively tunes hyperparameters controlling group-wise regularization.
The model shows practical applicability in both fMRI encoding and EEG decoding tasks.

Conclusions:

The presented framework offers a valuable tool for computational neuroscientists.
This approach facilitates more nuanced analysis of brain responses by allowing differential regularization.
Careful consideration of regularization's impact on interpretation is advised.