Spatiotemporally resolved multivariate pattern analysis for M/EEG.

ABSTRACT: An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion maps from these parameters to the equivalent decoding model, allowing predictions to be made about unseen test data

SUBMITTER: Higgins C 

PROVIDER: S-EPMC9188977 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature