Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Abstract

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3–45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25–63% improvement), and VEPs variability (16–44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI

“…The lower latency makes it possible to perform time-sensitive neurofeedback experiments, and the improved noise reduction will increase efficacy of closed-loop paradigms. Our work builds upon prior previous research describing techniques for real time BCG reduction ( Mayeli et al, 2016 ; Purdon et al, 2008 ; Steyrl et al, 2018 ; van der Meer et al, 2016 ), which are challenging to replicate independently, and advances and validates these approaches to achieve low latency and high-quality signals in real time. Our open-source, publicly available software package LLAMAS makes this technique freely available to the community in a simple to use, open-source MATLAB-based graphical interface.…”

“…Ideally, a highly effective BCG reduction technique, such as RLAS, could be implemented in real time, but RLAS depends on long sliding time windows (10 s or more). There have been previous efforts to resolve the latency and causality problems in these methods, for example by using Kalman Filters, though they have not seen widespread adoption in rtEEG-fMRI experiments ( Bonmassar et al, 2002 ; In et al, 2006 ; Masterton et al, 2007 ; Steyrl et al, 2018 ; D. 2017 ; Steyrl and Müller-Putz, 2018 ). The need for methods for BCG artifact correction is one of the primary impediments to further progress in EEG-fMRI research ( Perronnet et al, 2020 ).…”

EEG-LLAMAS: an open source, low latency, EEG-fMRI neurofeedback platform

“…However, many feedback paradigms require shorter latencies, for example detecting specific phases or events in the EEG, which cannot currently be achieved. There have been previous efforts to resolve the latency and causality problems in these methods, for example by using Kalman Filters, though none have seen widespread adoption in rtEEG-fMRI experiments (Bonmassar et al, 2002; In et al, 2006; Masterton et al, 2007; Steyrl et al, 2018, 2017; Steyrl and Müller-Putz, 2018). The need for methods for BCG artifact correction is one of the primary impediments to further progress in EEG-fMRI research (Perronnet et al, 2020).…”

Systematic evaluation of recursive approach of EEG‐segment‐based PCA for removal of helium‐pump artefact from MRI

“…The helium-pump system cannot be turned off during MRI experiments due to safety concerns and adverse effects on the quality of EEG and FMRI data [15]. Therefore, the use of either direct artefact recording [16], or post-processing methods is necessary. In this study, we chose a post-processing method, involving a recursive approach of EEG-segment-based principal component analysis (rsPCA), which has been proposed to remove helium-pump artefact [17].…”