Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Abstract

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

“…We found that there is significant residue from the cardioballistic artefact, and there are also remnants of electrooculographic and electromyographic artefacts. However, the found residues did not deviate from the standardly preprocessed recordings reported in [ 28 ]. In the future, it would be appropriate to consider the use of a methodology [ 28 ] to exclude detected clusters containing residues from further analysis of the EEG signal.…”

“…To validate the EEG preprocessing pipeline, our unsupervised clustering algorithm introduced in [ 28 ] was applied directly to standardly preprocessed EEG data, and the resulting clusters were compared to those obtained from a large dataset also described in [ 28 ]. It was confirmed that no additional artefacts or unique clusters were found.…”

“…Using our previously proposed method [ 28 ], we checked whether artefact residues remain in the EEG data. We found that there is significant residue from the cardioballistic artefact, and there are also remnants of electrooculographic and electromyographic artefacts.…”

“…Finally, a notch filter was applied, and the data were then band-pass-filtered between 0.5 and 30 Hz and referenced to the average signal obtained from two electrodes corresponding to mastoids, which is the most typical electrode reference in studies dealing with sleep data. For validation of preprocessed EEG data, our unsupervised clustering algorithm based on tSNE was applied to the data, and resulting clusters were visually checked and compared with resting-state datasets utilised in our previous study [ 28 ].…”

“…Here, we utilised the aOBS algorithm [ 27 ] exhibiting the most effective artefact suppression compared to the previously used approaches in sleep. Furthermore, we validated the data quality using our recent algorithm based on an application of a machine learning clustering algorithm called t-distributed stochastic neighbour embedding (t-SNE) [ 28 ].…”