Dynamic directed functional connectivity as a neural biomarker for objective motor skill assessment
Journal:
arXiv
Published Date:
Feb 19, 2025
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.
