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Gradient Artifact Correction for Simultaneous EEG- fMRI using Denoising Autoencoders

EEG recorded during MRI acquisition suffers from severe artifacts due to the imaging gradients. Here, we explore the possibility of using denoising autoencoders for correcting for these artifacts. After hyperparameter optimization, the performance of the algorithm was compared against PCA on two different synthesized datasets. The first dataset was created by adding a template artifact to clean EEG data and randomly shifting it in time to simulate aliasing. While the second dataset was formed by filtering out the EEG frequencies and adding a known ground-truth clean EEG signal. The performance of each method was assessed by the RMSE relative to the clean EEG signal. In addition, the correlation coefficient compared to the artifact signal was used to measure the residual artifact level. On the second synthesized dataset, denoising autoencoders outperformed PCA by 4.3% in terms of RMSE, meaning they were able to better preserve the original signal while at the same time the correlation with the underlying artifact was reduced by 40%. These preliminary results merit further investigation on a larger dataset.