Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95043
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Biomedical Engineering | en_US |
dc.contributor | Mainland Development Office | en_US |
dc.contributor | Department of Biomedical Engineering | - |
dc.contributor | University Research Facility in Behavioral and Systems Neuroscience | - |
dc.contributor | Mainland Development Office | - |
dc.contributor | Research Institute for Smart Ageing | - |
dc.creator | Zhang, J | en_US |
dc.creator | Huang, Y | en_US |
dc.creator | Ye, F | en_US |
dc.creator | Yang, B | en_US |
dc.creator | Li, Z | en_US |
dc.creator | Hu, X | en_US |
dc.date.accessioned | 2022-09-13T03:36:53Z | - |
dc.date.available | 2022-09-13T03:36:53Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/95043 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. | en_US |
dc.rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Zhang, J.; Huang, Y.; Ye, F.; Yang, B.; Li, Z.; Hu, X. Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning. Appl. Sci. 2022, 12, 4796 is available at https://doi.org/10.3390/app12094796. | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Evaluation | en_US |
dc.subject | Fine tactile sensation | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Stroke | en_US |
dc.title | Evaluation of post-stroke impairment in fine tactile sensation by electroencephalography (EEG)-based machine learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 9 | en_US |
dc.identifier.doi | 10.3390/app12094796 | en_US |
dcterms.abstract | Electroencephalography (EEG)-based measurements of fine tactile sensation produce large amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine-learning (ML) model with support vector machine (SVM) was established to automatically evaluate post-stroke impairments in fine tactile sensation. Stroke survivors (n = 12, stroke group) and unimpaired participants (n = 15, control group) received stimulations with cotton, nylon, and wool fabrics to the different upper limbs of a stroke participant and the dominant side of the control. The average and maximal values of relative spectral power (RSP) of EEG in the stimulations were used as the inputs to the SVM-ML model, which was first optimized for classification accuracies for different limb sides through hyperparameter selection (γ, C) in radial basis function (RBF) kernel and cross-validation during cotton stimulation. Model generalization was investigated by comparing accuracies during stimulations with different fabrics to different limbs. The highest accuracies were achieved with (γ = 21, C = 23) for the RBF kernel (76.8%) and six-fold cross-validation (75.4%), respectively, in the gamma band for cotton stimulation; these were selected as optimal parameters for the SVM-ML model. In model generalization, significant differences in the post-stroke fabric stimulation accuracies were shifted to higher (beta/gamma) bands. The EEG-based SVM-ML model generated results similar to manual evaluation of cortical responses to fabric stimulations; this may aid automatic assessments of post-stroke fine tactile sensations. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, May 2022, v. 12, no. 9, 4796 | en_US |
dcterms.isPartOf | Applied sciences | en_US |
dcterms.issued | 2022-05 | - |
dc.identifier.scopus | 2-s2.0-85130270823 | - |
dc.identifier.ros | 2021003275 | - |
dc.identifier.eissn | 2076-3417 | en_US |
dc.identifier.artn | 4796 | en_US |
dc.description.validate | 202209 bchy | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | CDCF_2021-2022 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China, China; Science and Technology Innovation Committee of Shenzhen, China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 68826257 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zhang_Evaluation_Post-Stroke_Impairment.pdf | 2.45 MB | Adobe PDF | View/Open |
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