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.
Bibliographical noteFunding Information:
The authors would like to thank Dr. Seung‐Schik Yoo for his constructive comments. This work was supported by the National Research Foundation (NRF), Korea, under project BK21.
© 2022 The Authors. Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
ASJC Scopus subject areas
- Electrical and Electronic Engineering