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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Detecting determinism in EEG signals using principal component analysis and surrogate data testing.

Amir H Meghdadi¹, Reza Fazel-Rezai, Yahya Aghakhani

¹Manitoba Univ., Winnipeg, Canada. meghdadi@ee.umanitoba.ca

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

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This study introduces a new method to detect determinism in noisy time series data. The novel approach successfully distinguishes between purely random and deterministic-yet-noisy signals, like EEG.

Area of Science:

Dynamical systems analysis
Signal processing
Neuroscience

Background:

Distinguishing deterministic from stochastic time series is challenging, especially with noise.
Existing methods often fail to detect underlying determinism in noisy signals.

Purpose of the Study:

To propose a novel method for identifying determinism in noisy time series.
To differentiate between purely stochastic and deterministic time series corrupted by noise.

Main Methods:

Extension of a smoothness analysis method in state space.
Utilizing surrogate data testing.
Defining partial smoothness indexes based on principal components.

Main Results:

The proposed method successfully distinguishes deterministic (even with noise) from stochastic time series.

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Last Updated: Jul 10, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Partial smoothness indexes indicate determinism when present.

Validated on simulated chaotic Lorenz time series and real EEG signals.

Conclusions:

The novel method effectively detects determinism in noisy time series.
EEG signals exhibit deterministic characteristics with stochastic components.
The method has potential applications in analyzing complex biological signals.