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

Abstract

The cryogenic pump is a crucial component of the magnetic resonance imaging (MRI) system for delivering liquid helium to a magnet for superconductivity, thereby generating a mechanical vibration. Thus, the cryogenic pump for liquid helium (helium pump) contaminates the electroencephalography (EEG) simultaneously acquired with functional MRI. The recursive approach of EEG-segment-based principal component analysis (rsPCA) has recently demonstrated its efficacy in removing this helium pump artefact. In the rsPCA, the recursion depth and EEG-segment size are crucial hyperparameters. rsPCA's performance and computational time across recursion depth (1, 2, 3) and segment sizes (165, 220, 265) are systematically evaluated. It is found that the recursion depth of 2 yielded significant reductions in the computational time compared to the depth of 3 across all segment sizes while maintaining the denoising performance. The binary classification performance (left-hand versus right-hand clenching) was also enhanced in this scenario (especially the use of EEG-segment size of 165 and 220) by using EEG gamma-band activity (30-50 Hz), which is predominantly contaminated by the helium-pump artefact.

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

“…Furthermore, one of the challenges of regression techniques is that they may not be effective in dealing with other artifacts, such as EMG artifacts [59], due to the lack of clear reference channels. With the emergence of potentially more efficient algorithms such as principal component analysis (PCA) and independent component analysis (ICA) [60,61], the regression method is no longer the default choice for removing artifacts from an EEG caused by EOG or ECG signals.…”

“…Furthermore, one of the challenges of regression techniques is that they may not be effective in dealing with other artifacts, such as EMG artifacts [ 59 ], due to the lack of clear reference channels. With the emergence of potentially more efficient algorithms such as principal component analysis (PCA) and independent component analysis (ICA) [ 60 , 61 ], the regression method is no longer the default choice for removing artifacts from an EEG caused by EOG or ECG signals. Algorithm 2 Regression-based denoising of EEG signals Input: EEG signal X , artifact signal Y Output: Clean EEG signal Z function Regression ( ) Calculate regression coefficients between X and Y Remove artifact from EEG signal return Clean EEG signal Z end function …”