Reference layer adaptive filtering (RLAF) in simultaneous EEG-fMRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods: high temporal resolution of EEG and high spatial resolution of fMRI. EEG recordings are, however, afflicted by severe artifacts caused by fMRI scanners. Average artifact subtraction (AAS) is a common method to reduce those artifacts. Recently, we introduced an add-on method that uses a reusable reference layer EEG cap prototype in combination with adaptive filtering, to improve EEG data quality substantially. The methods applies adaptive filtering with reference layer artefact data to optimize artefact subtraction from EEG and is named reference layer adaptive filtering (RLAF).