Unleashing the potential of OPM-MEG to study event-related fields against low-frequency artifacts: the case of sentence processing

OPM-MEG (optically pumped magnetometers-magnetoencephalography) offers unprecedented opportunity in its proximity to the brain and in mobility, outperforming other human MEG systems. However, movements induce low-frequency artifacts (up to ∼3 Hz) in this system, calling for pipelines that could efficiently reduce these low-frequency noises. Although a high-pass filter of e.g., 4 Hz may minimize these artifacts, it may also eliminate many event-related field (ERF) components, such as the N400 response in sentence processing studies. Moreover, as an emerging technology with expensive sensors, many labs are starting with an experimental setup with fewer sensors (e.g., ∼10), making it challenging to reject principal components based on visually inspecting the topographic field maps. In the current paper, we show that a combination of moderate high-pass filtering (1 Hz) and evoked-biased denoising source separation (evoked-biased DSS) can effectively reveal typical N400 deflections and effects (between nouns and verbs), with a nine-sensor OPM-MEG setup. Our current pipeline paves the way to studying ERFs with OPM-MEG in more budget-friendly setups with a small number of sensors.