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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

A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks.

M Thilaga¹, R Vijayalakshmi¹, R Nadarajan¹

¹* Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India.

Journal of Integrative Neuroscience

|July 13, 2016

Summary

This study introduces a new Common Functional Pattern Mining method to reveal similar brain region interaction patterns during cognitive tasks. The approach effectively identifies common functional patterns in electroencephalography (EEG) data across different cognitive loads.

Keywords:

EEG Functional brain networks cognition graph pattern mining graph theory mutual information network metrics

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Last Updated: Mar 18, 2026

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

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Area of Science:

Neuroscience
Computational Biology
Data Mining

Background:

Understanding complex neuronal interactions in the human brain is challenging.
Innovative mathematical and computational models are needed to study brain region activity during cognitive tasks.

Purpose of the Study:

To present a novel Common Functional Pattern Mining approach.
To demonstrate similar interaction patterns arising from common behaviors of brain regions.

Main Methods:

Modeled electroencephalography (EEG)-based functional brain network electrode sites as transactions.
Represented node-based complex network measures as itemsets.
Transformed itemsets into a Functional Pattern Graph for mining.

Main Results:

Identified common functional patterns related to specific brain functioning.
Empirically demonstrated the approach's efficiency in detecting similarities between electrode sites.
Showcased pattern identification across various cognitive load states.

Conclusions:

The Common Functional Pattern Mining approach effectively reveals underlying mechanisms of neuronal activity.
This method aids in understanding brain region interactions during different cognitive states.
The approach provides insights into functional brain network dynamics.