BCMI-Driven Motion Control Detection: EEG-Based Machine Learning and Interaction Entropy for High-Order Brain Networks

TL;DR

This paper presents a novel EEG-based machine learning approach using higher-order brain network analysis to detect motor control states during music-assisted simulated driving, revealing enhanced connectivity and entropy modulated by music.

Contribution

It introduces a dynamic higher-order network model with cross-information entropy for EEG analysis, advancing beyond static methods to better understand music's impact on brain networks during complex tasks.

Findings

Enhanced third-order connectivity during music-stimulated driving

Higher-order information entropy increases with music influence

Strong correlation between machine learning accuracy and brain network features

Abstract

This study investigates the cognitive motor control detection and the underlying neuroregulatory mechanisms during music-assisted simulated driving. Using a dynamic higher-order network model constructed with EEG-based cross-information entropy, we quantify the dynamic coordination within brain networks activated during both music listening and driving. This approach, which contrasts with previous static network analyses, provides novel insights into how musical stimuli modulate the complex interplay of brain regions during demanding tasks. Results demonstrated enhanced third-order connectivity and elevated higher-order information entropy in music-stimulated driving compared to baseline driving, as evidenced by increasing Phi values of higher-order network indices. Supervised machine learning, including support vector machines, revealed a strong correlation between model accuracy and ROC-AUC values and the hierarchy of brain network features. This underscores the importance of higher-order features in decoding brain motor-control states during music-simulated driving. These findings deepen our understanding of the interplay between music cognition and motor control, offering valuable insights for the development of novel brain-computer-music interfaces (BCMI) and adaptive human-machine systems to enhance performance in demanding tasks like driving.