Classification of error-related potentials evoked during stroke rehabilitation training
Open accessAbstract
Objective. Error-related potentials (ErrPs) are elicited in the human brain following an error’s perception. Recently, ErrPs have been observed in a novel task situation, i.e. when stroke patients perform upper-limb rehabilitation exercises. These ErrPs can be used to develop assist-as-needed (AAN) robotic stroke rehabilitation systems. However, to date, there is no reported research on assessing the feasibility of using the ErrPs to implement the AAN approach. Hence, in this study, we evaluated and compared the single-trial classification of novel ErrPs using various classical machine learning and deep learning approaches. Approach. Electroencephalogram data of 13 stroke patients recorded while performing an upper-limb physical rehabilitation exercise were used. Two classification approaches, one combining the xDAWN spatial filtering and support vector machines, and the other using a convolutional neural network-based double transfer learning, were utilized. Main results. Results showed that the ErrPs could be detected with a mean area under the receiver operating characteristics curve of 0.838, and a mean accuracy of 0.842, 0.257 above the chance level (p < 0.05), for a within-subject classification. The results indicated the feasibility of using ErrP signals in real-time AAN robot therapy with evidence from the conducted latency analysis, cross-subject classification, and three-class asynchronous classification. Significance. The findings presented support our proposed approach of using ErrPs as a measure to trigger and/or modulate as required the robotic assistance in a real-time human-in-the-loop robotic stroke rehabilitation system.
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Calibration-free cross-subject ErrP classification has been demonstrated with generalized LDA, an xDAWN+RG pipeline with a support vector machine classifier, ensemble classifiers, and online PCA-based pipelines, though without inherent interpretability.
“Calibration-free cross-subject ErrP classification has been demonstrated with generalized LDA [16], an xDAWN+RG pipeline with a support vector machine classifier [17], ensemble classifiers [18], and online PCA-based pipelines [19], though without inherent interpretability.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [17]
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- Authors
- Kumar, A., Pirogova, E., Mahmoud, S. S., & Fang, Q.
- Affiliations
- Shantou University (CN)
- RMIT University (AU)
- Corresponding author
- Fang, Qiang
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-09-02
- Volume
- 18
- Issue
- 5
- Pages
- 056022
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3193050378
- Cited by
- 17
- References
- 50
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 4
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- Funders
- Li Ka Shing Foundation (Grant No. 2020LKSFG03C)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- error-related potentials, stroke rehabilitation, upper limb rehabilitation, EEG classification, support vector machine, convolutional neural networks, error detection
- MeSH terms
- Stroke Rehabilitation, Brain, Electroencephalography, Humans, Stroke, Support Vector Machine, Brain-Computer Interfaces
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