How to improve the muscle synergy analysis methodology?

Area of Science:

• Neuroscience
• Robotics
• Rehabilitation Science
• Sport Science

Background:

• Muscle synergy analysis is vital for understanding motor coordination across various scientific fields.
• Dimensionality reduction techniques are employed to identify patterns in muscle activation.
• Variability in analysis outcomes necessitates methodological clarification.

Purpose of the Study:

• To clarify methodological aspects of muscle synergy analysis.
• To highlight the impact of data quality and processing on synergy estimates.
• To provide recommendations for robust muscle synergy identification.

Main Methods:

• Review and analysis of existing muscle synergy methodologies.
• Emphasis on the importance of electromyography (EMG) data quality, including normalization, noise removal, and filtering.
• Discussion of spatial, temporal, and spatio-temporal synergy formulations.
• Exploration of criteria for selecting the number of synergies.

Main Results:

• Muscle synergy analysis is sensitive to EMG data quality (normalization, filtering, noise).
• Sufficient muscle activation is required to fully explore the synergy subspace.
• Concurrent use of spatial, temporal, and spatio-temporal synergies is recommended.
• Criteria beyond variance thresholds, such as noise estimates and reliability, are valuable for selecting the number of synergies.

Conclusions:

• Muscle synergy analysis is a robust statistical tool but requires careful attention to methodological details.
• Optimizing EMG data processing and employing diverse synergy formulations enhance the reliability of motor coordination insights.
• Alternative criteria for determining the number of synergies improve analytical rigor.