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

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

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 classifica...

Full description

Bibliographic Details
Main Authors: David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:NeuroImage
Subjects:
MEG/EEG
Neuronal oscillations
Machine learning
Covariance
Spatial filters
Riemannian geometry
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920303797

Similar Items

Similar Items