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Related Experiment Video

Updated: Jan 9, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Predicting Gait Speed Using Brain Functional Connectivity Maps During Walking.

Rateb Katmah, Aamna AlShehhi, Doua Kosaji

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

|December 3, 2025

Summary

This summary is machine-generated.

This study links brain connectivity to walking speed using EEG and AI. The findings suggest brain activity patterns can help predict gait, aiding in diagnosing movement disorders.

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Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Understanding the neural control of locomotion is crucial for diagnosing and treating gait abnormalities.
Brain functional connectivity plays a vital role in regulating gait dynamics.

Purpose of the Study:

To quantitatively investigate the association between brain functional connectivity and gait speed.
To develop AI-based models for predicting gait speed using EEG-derived connectivity maps.

Main Methods:

Collected gait and electroencephalography (EEG) data from 8 healthy participants at varying speeds.
Generated brain functional connectivity maps using Partial Directed Coherence (PDC).
Utilized a convolutional neural network (CNN) with Leave-One-Subject-Out cross-validation for model evaluation.

Main Results:

The CNN model achieved an average classification accuracy of 60.87% in predicting gait speed.
Higher precision (0.76) and F1 scores (0.64) were observed at faster gait speeds.
The results indicate AI's capability in reflecting motor control through neural network analysis.

Conclusions:

Brain functional connectivity is significantly associated with gait speed.
Integrating EEG-derived connectivity and AI models offers a novel approach for gait analysis.
This approach holds potential for personalized gait diagnostics and rehabilitation strategies.