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Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling.

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Summary

Dataset shifts in neuroscience hinder machine learning (ML). This study harmonizes M/EEG data using domain adaptation, significantly improving ML model generalization across diverse datasets for brain age prediction.

Keywords:
MEG/EEGRiemannian geometrybrain agedataset shiftdomain adaptationmachine learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Neuroscience studies require large datasets for machine learning (ML), but data heterogeneity (dataset shifts) across sites, devices, or protocols limits ML model generalization.
  • Magnetoencephalography (M/EEG) data are often represented by covariance matrices, which are sensitive to dataset shifts caused by variations in brain activity, anatomy, or equipment.

Purpose of the Study:

  • To investigate the impact of dataset shifts on M/EEG data and evaluate domain adaptation methods for improving ML model generalization.
  • To theoretically explain how anatomical, physiological, and technical factors induce covariance shifts in M/EEG data.

Main Methods:

  • Developed and evaluated model-based dataset alignment methods (re-centering, re-scaling, rotation correction) leveraging the geometry of covariance matrices.
  • Applied and assessed these alignment methods on MEG and EEG datasets for brain age prediction tasks.
  • Utilized controlled simulations to theoretically validate the alignment methods' effectiveness in addressing covariance shifts.

Main Results:

  • Paired rotation correction was crucial for M/EEG data from the same subjects under different tasks (e.g., rest vs. passive/smt tasks), improving R-squared by up to 0.17.
  • Re-centering improved performance when including unseen subjects and different tasks (e.g., +0.096 R-squared for rest-passive).
  • Re-centering was essential for generalizing brain age prediction to independent datasets from different populations and devices, achieving performance close to within-dataset predictions.

Conclusions:

  • Domain adaptation techniques, particularly re-centering and rotation correction, can significantly enhance the generalization of M/EEG-based regression models across diverse datasets.
  • Statistically harmonizing M/EEG data through alignment methods is critical for successful cross-dataset ML applications in neuroscience.
  • This approach facilitates the integration of large public datasets, overcoming limitations imposed by dataset shifts.