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MEEGqc - a quality control workflow for MEEG data
Aaron Reer, Evgeniia Gapotsneva
Presenting author:
Aaron Reer
Over the last decades non-invasive neuroimaging methods such as (f)MRI and MEG or EEG have grown in popularity in the neuroscientific community. This yields large amounts of data being acquired at research facilities around the globe. Such data is susceptible to artifacts, especially in the domain of MEEG, which can severely corrupt the data quality. These can either be external noise sources, e.g. powerline artifacts, or internal noise like data contamination due to eye movements of the subject. For this reason, quality assessment of the data is an important step to produce valid and reproducible science. However, the visual detection and annotation of artifacts in MEEG data requires expertise, is a tedious and time extensive task and hardly resembles a standardized procedure. Since artifact detection is commonly done manually it might also be subject to biases induced by the person inspecting the data. Hence an automated approach for artifact detection is desirable, especially with respect to the tendency of ever-growing datasets.
To address this issue we will provide software for assessing the quality of MEEG data by detecting artifacts in the data and visualizing them in easily interpretable reports. We will thus make datasets comparable among each other by using standardized metrics to assess the quality of the data, i.e. the severity of the artifacts. Since neuroimaging data can be very diverse with respect to its structural organization the software will be tailored to the BIDS standard and set up as a BIDS-app. BIDS is a community driven effort to unify the storage of neuroimaging datasets. This work is inspired by a similar software for standardized quality control in the domain of fMRI, called mriqc.