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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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Wavelet-based visual compass.

Stefan Meyer¹, James C Knight¹, Alexander Dewar²

¹Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom.

|April 7, 2026

Summary

This summary is machine-generated.

Ants use visual snapshots for navigation. Pre-processing images with Haar wavelets improves route following robustness, even with environmental changes, by highlighting distant objects.

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Area of Science:

Biomimetics
Neuroscience
Robotics

Background:

Ants are exceptional visual navigators, inspiring biomimetic robotics.
Visual compass-style snapshot models use stored images for route following.
Haar wavelets pre-processing enhances snapshot robustness.

Purpose of the Study:

Investigate properties and limits of bearing recovery using visual navigation.
Compare wavelet-processed, edge-processed, and raw images under perturbations.
Analyze localized spatial-frequency encodings for route following.

Main Methods:

Utilized Haar wavelets for image pre-processing.
Compared image resolutions and processing techniques (wavelets, edge, raw).
Assessed performance under naturalistic environmental perturbations.

Main Results:

High-frequency wavelet coefficients highlight distant objects.
Resolution decrease blurs distant objects, diminishing wavelet effectiveness.
Environmental perturbations are compensated by appropriate image processing.

Conclusions:

Localized spatial frequency analysis is crucial for robust snapshot navigation.
Image processing choice significantly impacts navigation accuracy and resilience.
Findings advance understanding of ant navigation and robotic applications.