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
License
All rights reserved
File Access Level
Open
ISBN9798350324471
Output statusPublished
Publication dates
Online11 Dec 2023
Publication process dates
AcceptedApr 2023
Deposited06 Mar 2024
Permalink -

https://repository.derby.ac.uk/item/q32v2/analysis-of-artifactual-components-rejection-threshold-towards-enhanced-characterization-of-neural-activity-in-post-stroke-survivor

Download files


Accepted author manuscript
Manuscript-EMBC2023_AMG_UDORA_Uploaded.pdf
License: All rights reserved
File access level: Open

  • 2
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657
Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography
Zhang, H., Peng, B., Tian, L., Samuel, O. and Li, G. 2024. Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography. Cyborg and Bionic Systems. pp. 1-11. https://doi.org/10.34133/cbsystems.0094
An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection
Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., Zhao, G., Samuel, O. and Chen, S. 2023. An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection. Journal of Information Security and Applications. 80, pp. 1-17. https://doi.org/10.1016/j.jisa.2023.103654
Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters
Awomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H. 2023. Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters. City and Environment Interactions. 20, pp. 1-12. https://doi.org/10.1016/j.cacint.2023.100127
A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition
Kulwa, F., Zhang, H., Samuel, O., Asogbon, M., Scheme, E., Kushaba, R., McEwan, A. and Li, G. 2023. A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition. IEEE Transactions on Human-Machine Systems. pp. 1-12. https://doi.org/10.1109/THMS.2023.3329536
An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals
Zangene, A., Samuel, O., Abbasi, A., Nazarpour, K., McEwan, A. and Li, G. 2023. An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340791
Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors
Samuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G. 2023. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340683
Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Samuel, O.W., Li, J., Kulwa, F., Kandwal, A. and Nie, Z. 2023. Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE International Workshop on Medical Measurement and Applications (MEMEA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171941
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems
Kulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G. 2023. A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems. 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171910
An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running
Zangene, A. R., Samuel, O., Abbasi, A., McEwan, A., Asogbon, M. G., Li, G. and Nazarpour, K. 2023. An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running. Biomedical Signal Processing and Control. 86 (B), pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105103
A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution
Li, F., Han, F., Wang, L., Huang, L., Samuel, O.W., Zhao, H., Xie, R., Wang, P., Tian, Q., Li, Q., Zhao, Y., Yu, Mei, Sun, J., Yang, R., Zhou, X., Li, F., Li, G., Lu, Y., Guo, P. and Liu, Z. 2023. A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution. Advanced Functional Materials. 2300859, pp. 1-9. https://doi.org/10.1002/adfm.202300859
On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines
Nsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O. 2023. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthcare Technology Letters. 10 (1-2), pp. 11-22. https://doi.org/10.1049/htl2.12044
Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition
Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G. 2023. Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition. Nature Scientific Data. 10 (358), pp. 1-16. https://doi.org/10.1038/s41597-023-02263-3
A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals
Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S. 2023. A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2023.3265983