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Related Experiment Video

Updated: Feb 12, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Brain complexity in motion: Multiscale entropy analysis on mobile EEG data to assess motor performance.

Daghan Piskin1, Gjergji Cobani2, Daniel Büchel3

  • 1Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn, Germany.

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|February 10, 2026
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Summary

Multiscale entropy (MSE) effectively measures brain complexity during movement. This study shows MSE links brain activity patterns to kicking performance and expertise levels using mobile electroencephalography (EEG).

Keywords:
Brain complexityMobile electroencephalographyMotor performanceMovementMultiscale entropy

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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.