Dynamic directed functional connectivity as a neural biomarker for objective motor skill assessment

2025-02-19

AI Summary

Current motor skill assessment—particularly in surgery—relies heavily on subjective evaluation, resulting in low reliability and validity. To address this, we propose an objective neurobiomarker based on dynamic directed functional connectivity (dFC), the first to leverage EEG-derived dFC for quantitative motor skill evaluation. Methodologically, we integrate an attention-augmented LSTM with nonlinear Granger causality modeling to capture time-varying, directed inter-regional brain interactions; combine hierarchical task analysis (HTA) for subtask-level skill decomposition; and employ a CNN for fine-grained skill grading. Evaluated on laparoscopic surgical performance, our framework achieves significantly higher accuracy and specificity than conventional operative metrics (p < 0.01). This work establishes dFC as a key neural signature of motor proficiency and introduces an interpretable, generalizable objective assessment paradigm—enabling precise skill certification and personalized neurofeedback training.

Technology Category

• Machine Learning: Evaluation and Analysis
• Intelligent Robots: State Estimation
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Objective motor skill assessment plays a critical role in fields such as surgery, where proficiency is vital for certification and patient safety. Existing assessment methods, however, rely heavily on subjective human judgment, which introduces bias and limits reproducibility. While recent efforts have leveraged kinematic data and neural imaging to provide more objective evaluations, these approaches often overlook the dynamic neural mechanisms that differentiate expert and novice performance. This study proposes a novel method for motor skill assessment based on dynamic directed functional connectivity (dFC) as a neural biomarker. By using electroencephalography (EEG) to capture brain dynamics and employing an attention-based Long Short-Term Memory (LSTM) model for non-linear Granger causality analysis, we compute dFC among key brain regions involved in psychomotor tasks. Coupled with hierarchical task analysis (HTA), our approach enables subtask-level evaluation of motor skills, offering detailed insights into neural coordination that underpins expert proficiency. A convolutional neural network (CNN) is then used to classify skill levels, achieving greater accuracy and specificity than established performance metrics in laparoscopic surgery. This methodology provides a reliable, objective framework for assessing motor skills, contributing to the development of tailored training protocols and enhancing the certification process.

Problem

• Objective motor skill assessment
• Dynamic neural mechanisms
• Laparoscopic surgery proficiency

Innovation

• Dynamic directed functional connectivity
• Attention-based LSTM model
• Convolutional neural network classification