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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

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Directed information measure for quantifying the information flow in the brain.

Ying Liu¹, Selin Aviyente

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. liuying5@egr.msu.edu

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

|December 8, 2009

Summary

This study introduces directed information to measure brain signal causality, improving upon traditional methods by accounting for nonlinear interactions in electroencephalogram (EEG) data.

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Area of Science:

Neuroscience
Information Theory

Background:

Quantifying causal relationships between neuronal sites is crucial in neurophysiology.
Existing methods like Granger causality often rely on linear models and fail to capture nonlinear dependencies in multi-channel EEG signals.

Purpose of the Study:

To propose a novel method for quantifying causality in neuronal interactions using directed information (DI).
To address the limitations of existing methods by incorporating nonlinear dependencies in EEG data analysis.

Main Methods:

Utilized the directed information (DI) criterion to measure information flow between neuronal signals.
Applied the DI measure to real electroencephalogram (EEG) data from control and schizophrenic subjects.
Employed the Fourier bootstrapping technique to validate the statistical significance of the computed DI values.

Main Results:

The directed information measure was successfully applied to analyze causality in real EEG data.
The study demonstrated the ability of DI to capture information flow, potentially revealing complex interactions in brain activity.
Statistical significance of DI values was confirmed, supporting the reliability of the findings.

Conclusions:

Directed information offers a robust approach to quantifying causality in neurophysiological signals, surpassing traditional linear methods.
This method provides a valuable tool for investigating brain connectivity and understanding alterations in conditions like schizophrenia.
Further research can explore the application of DI in diverse neuroscientific contexts and complex biological systems.