A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface Article Swipe
Jing Jin
,
Hao Sun
,
Ian Daly
,
Shurui Li
,
Chang Liu
,
Xingyu Wang
,
Andrzej Cichocki
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2021.3139095
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2021.3139095
The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.
Related Topics
Concepts
Brain–computer interface
Motor imagery
Computer science
Artificial intelligence
Pattern recognition (psychology)
Classifier (UML)
Graph embedding
Graph
Support vector machine
Embedding
Multiclass classification
Electroencephalography
Machine learning
Theoretical computer science
Psychology
Psychiatry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsre.2021.3139095
- https://ieeexplore.ieee.org/ielx7/7333/9695946/09664515.pdf
- OA Status
- diamond
- Cited By
- 50
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200285824
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4200285824Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tnsre.2021.3139095Digital Object Identifier
- Title
-
A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer InterfaceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-28Full publication date if available
- Authors
-
Jing Jin, Hao Sun, Ian Daly, Shurui Li, Chang Liu, Xingyu Wang, Andrzej CichockiList of authors in order
- Landing page
-
https://doi.org/10.1109/tnsre.2021.3139095Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/7333/9695946/09664515.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/7333/9695946/09664515.pdfDirect OA link when available
- Concepts
-
Brain–computer interface, Motor imagery, Computer science, Artificial intelligence, Pattern recognition (psychology), Classifier (UML), Graph embedding, Graph, Support vector machine, Embedding, Multiclass classification, Electroencephalography, Machine learning, Theoretical computer science, Psychology, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
50Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 18, 2024: 12, 2023: 12, 2022: 8Per-year citation counts (last 5 years)
- References (count)
-
69Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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