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

Multiscale entropy (MSE) as a measure of brain complexity provides substantial insights into the adaptability of the brain. However, it is often applied to resting-state electroencephalography (EEG) or in static tasks. The current study assessed the reliability, validity and classification accuracy of MSE computed on mobile EEG data for linking brain complexity to motor performance within a kicking task. Eleven novice participants underwent repeated measurements to assess test-retest reliability, while the data from 15 novices and 15 football players were used to evaluate known-groups validity, convergent validity and classification accuracy. EEG data were recorded using 65 active electrodes and MSE estimates were computed for 64 time scales on preprocessed data. Results showed poor to excellent reliability for MSE estimates, exhibiting channel- and scale-specific variations, with reliability generally higher at fine-to-mid scales. Experts exhibited significantly lower entropy at coarse scales in left frontal and at fine scales in centroparietal regions compared to novices. Negative correlations were found between entropy estimates and kicking accuracy. Receiver operating characteristic curves of entropy estimates and their principal components demonstrated moderate to good classification accuracy between expertise levels. These findings suggest MSE as a promising metric for investigating brain complexity in movement contexts, revealing distinct patterns of complexity associated with motor performance. Future research across diverse tasks and populations is crucial to further elucidate this relationship and explore the applied potential of MSE.