Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling.

ABSTRACT: A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data

SUBMITTER: Ravishankar S 

PROVIDER: S-EPMC8632362 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature