Brain complexity in motion: Multiscale entropy analysis on mobile EEG data to assess motor performance

Area of Science:

• Neuroscience
• Motor Control
• Signal Processing

Background:

• Multiscale entropy (MSE) quantifies brain complexity, offering insights into neural adaptability.
• Current applications of MSE are limited to resting-state or static tasks, restricting its use in dynamic motor contexts.
• Mobile electroencephalography (EEG) provides a feasible platform for studying brain activity during real-world tasks.

Purpose of the Study:

• To assess the reliability, validity, and classification accuracy of MSE derived from mobile EEG data.
• To investigate the relationship between brain complexity, measured by MSE, and motor performance in a kicking task.
• To differentiate brain complexity patterns between novice and expert performers.

Main Methods:

• Computed MSE across 64 time scales using mobile EEG data from 65 active electrodes.
• Assessed test-retest reliability with repeated measurements in 11 novices.
• Evaluated known-groups validity, convergent validity, and classification accuracy using data from 15 novices and 15 football players.

Main Results:

• MSE estimates demonstrated variable reliability (poor to excellent), generally higher at fine-to-mid scales.
• Experts showed significantly lower entropy at coarse scales (left frontal) and fine scales (centroparietal) compared to novices.
• Negative correlations between entropy and kicking accuracy were observed.
• Receiver operating characteristic (ROC) analysis indicated moderate to good classification accuracy between expertise levels.

Conclusions:

• MSE computed on mobile EEG is a promising metric for assessing brain complexity during motor tasks.
• Distinct patterns of brain complexity are associated with varying levels of motor performance and expertise.
• Further research is needed to explore MSE's potential in diverse tasks and populations.