Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor

Conference paper

Asogbon, M., Huai, Y., Samuel, O., Jing, Z., Ma, Y., Liu, J., Jiang, Y., Fu, Y., Li, G. and Li, Y. 2023. Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE Xplore. https://doi.org/https://doi.org/10.1109/EMBC40787.2023.10340688
AuthorsAsogbon, M., Huai, Y., Samuel, O., Jing, Z., Ma, Y., Liu, J., Jiang, Y., Fu, Y., Li, G. and Li, Y.
TypeConference paper
Abstract

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for motor skill recovery in stroke patients. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices.

KeywordsStroke Rehabilitation; Electroencephalography; Biomedical Signal Processing; Brain-computer Interface; Pattern Recognition
Year2023
Conference45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society
PublisherIEEE Xplore
ISSN2694-0604
Digital Object Identifier (DOI)https://doi.org/https://doi.org/10.1109/EMBC40787.2023.10340688
Web address (URL)https://ieeexplore.ieee.org/abstract/document/10340688
Accepted author manuscript
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All rights reserved
File Access Level
Open
ISBN9798350324471
Output statusPublished
Publication dates
Online11 Dec 2023
Publication process dates
AcceptedApr 2023
Deposited06 Mar 2024
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