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

TL;DR

This paper introduces a novel EEG-based neural biomarker using dynamic directed functional connectivity and deep learning to objectively assess motor skills, surpassing traditional subjective and kinematic methods.

Contribution

It presents a new approach combining EEG, attention-based LSTM, and CNN to evaluate motor skills at subtask levels, improving accuracy and objectivity in skill assessment.

Findings

Achieved higher classification accuracy than existing metrics.

Demonstrated neural connectivity patterns differentiate skill levels.

Provided detailed neural insights into expert versus novice performance.

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.