Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

• Published in: NeuroImage (Orlando, Fla.) Vol. 222; p. 116893
• Main Authors: Sabbagh, David, Ablin, Pierre, Varoquaux, Gaël, Gramfort, Alexandre, Engemann, Denis A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2020; Elsevier Limited; Elsevier
• Subjects: Adult; Anatomy; Artificial Intelligence; Brain Waves - physiology; Cerebral Cortex - physiology; Clinical outcomes; Cognitive ability; Cognitive science; Computer Science; Computer Simulation; Covariance; Discriminant analysis; EEG; Electroencephalography; Electroencephalography - methods; Electromyography; Embedding; Humans; Interfaces; Learning algorithms; Life Sciences; Machine Learning; Magnetoencephalography; Magnetoencephalography - methods; Mathematical analysis; Mathematical models; MEG/EEG; Models, Theoretical; Neuronal oscillations; Neurons and Cognition; Neuroscience; Prediction models; Psychology and behavior; Regression Analysis; Riemannian geometry; Sensors; Signal Processing, Computer-Assisted; Spatial filters; Statistical analysis; Statistics; Supervised Machine Learning; Riemannian geometry; Spatial filters; Neuronal oscillations; MEG/EEG; Covariance; Machine learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116893

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning. •We propose models for regression on M/EEG signals without source localization.•SPoC spatial filtering and Riemannian embedding support subject-level prediction.•We validate models with simulations and analysis of ~600 MEG and ~1000 EEG recordings.•SPoC is practical for event-level prediction.•The Riemannian model is more sensitive to anatomy and more robust to noise.