Unlocking Insights from the Human Body
Explore a critical review of multimodal biomedical datasets, including EEG, ECG, PPG, and fNIRS. Navigate challenges and learn how to leverage these resources for impactful research.
Signal Explorer
Click on a signal type to explore its unique characteristics, common uses, and potential artifacts. This section provides a high-level comparison to understand the strengths and weaknesses of each modality.
Dataset Deep-Dive
Explore prominent multimodal datasets. Use the filters to find datasets containing specific signal combinations, and click on any card to see more detailed information about its acquisition and key features.
The Core Challenges
Successfully using multimodal data requires overcoming two fundamental hurdles: ensuring high signal quality despite noise and managing incomplete data. This section visualizes these key challenges.
Challenge 1: Signal Quality & The Real World
A fundamental tension exists between collecting pristine data in a lab and capturing ecologically valid data in the real world. As collection settings become more naturalistic, signals become more susceptible to noise and motion artifacts.
Challenge 2: The Missing Modality Problem
Due to sensor failure, cost, or participant burden, it's common for datasets to have incomplete information—where one or more signal types are missing for a given trial. This requires specialized analytical methods that can handle incomplete data gracefully.
Example: A grid where green indicates a present modality and red (X) indicates a missing one.
Key Recommendations for Researchers
Based on the comprehensive analysis, here are actionable recommendations for effectively utilizing and contributing to the ecosystem of multimodal physiological datasets.
Prioritize Standards
Seek datasets from established repositories like PhysioNet and OpenNeuro that adhere to FAIR, TRUST, and BIDS standards for higher quality and reusability.
Scrutinize Protocols
Meticulously review acquisition parameters (sampling rates, sensors) and contextual metadata to fully understand the data's characteristics and limitations.
Seek "Ground Truth"
For signal processing research, prioritize datasets with "ground truth" or reference signals to objectively benchmark and validate your algorithms.
Prepare for Artifacts
Acknowledge that real-world data is noisy. Be prepared to implement robust artifact identification and removal techniques as a core part of your analysis pipeline.
Account for Completeness
Be aware of sample sizes and the "missing modality problem." Explore advanced techniques (e.g., MLMM) that can handle incomplete data to maximize utility.
Leverage Benchmarks
Consult frameworks like BenchNIRS and CLIMB to gain a standardized perspective on dataset quality and guide your choice of analytical methods.