Statistical threshold for nonlinear Granger Causality in motor intention analysis.

Area of Science:

• Neuroscience
• Systems Biology
• Signal Processing

Background:

• Understanding large dynamic systems requires analyzing directed influences between multiple signal measurements.
• Functional connectivity analysis is crucial for dissecting complex multi-variable systems.

Purpose of the Study:

• To develop and apply a directional flow measure for analyzing functional connectivity in large, complex systems.
• To extract relevant information from causality maps using a novel thresholding method.
• To validate the approach in a brain-computer interface (BCI) application.

Main Methods:

• Utilized nonlinear Granger Causality (GC) based on nonlinear predictive models with radial basis functions (RBF).
• Implemented a spatial statistical thresholding method, displaying the top 20% of causality pathways.
• Applied the method to 128-channel surface electroencephalography (EEG) data for decoding intended arm movements (left, right, forward).

Main Results:

• The directional flow measure effectively extracted functional connectivity information.
• The thresholding method successfully highlighted key causal pathways.
• Causal influence directions from active brain regions were unique for each intended arm movement.

Conclusions:

• The proposed nonlinear Granger Causality with thresholding is a viable method for analyzing functional connectivity in complex systems.
• This approach demonstrates potential for decoding motor intentions in brain-computer interface applications.
• Optimizing the radius selection in the region of interest is important for accurate causal influence mapping.