Temporal Synchronization of Multimodal Hyperscanning Recordings: Challenges, Methodologies, and Best Practices

Area of Science:

• Neuroscience
• Biomedical Engineering
• Data Science

Background:

• Multimodal hyperscanning involves simultaneous data collection from multiple participants using various sensors.
• Accurate temporal synchronization across all sensor nodes is essential for analyzing inter-participant and inter-modal temporal dynamics.

Purpose of the Study:

• To provide a comprehensive overview of challenges, methods, and best practices for temporal synchronization in multimodal hyperscanning.
• To guide researchers in optimizing experimental designs for precise data acquisition.

Main Methods:

• Discussion of various temporal synchronization techniques.
• Analysis of challenges inherent in synchronizing multiple sensor nodes.
• Consideration of sampling clock mismatches and their effects.
• Guidance on quantifying trigger offsets and noise.

Main Results:

• Identification of key challenges in multimodal hyperscanning synchronization.
• Evaluation of different synchronization methods and their suitability.
• Quantification of the impact of clock mismatches on data integrity.
• Framework for assessing trigger offsets and noise levels.

Conclusions:

• Effective temporal synchronization is critical for valid multimodal hyperscanning data analysis.
• Adherence to best practices and careful consideration of synchronization methods enhance data quality.
• Understanding and mitigating synchronization errors improve the reliability of findings in hyperscanning research.