[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation]. Article Swipe
Lincong Pan
,
Xinwei Sun
,
Kun Wang
,
Yuzhen Cao
,
Minpeng Xu
,
Dong Ming
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202411035
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202411035
Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/40288968
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409905527
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409905527Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7507/1001-5515.202411035Digital Object Identifier
- Title
-
[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-25Full publication date if available
- Authors
-
Lincong Pan, Xinwei Sun, Kun Wang, Yuzhen Cao, Minpeng Xu, Dong MingList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/40288968Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/12035623Direct OA link when available
- Concepts
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Electroencephalography, Session (web analytics), Decoding methods, Motor imagery, Psychology, Adaptation (eye), Cognitive psychology, Computer science, Artificial intelligence, Brain–computer interface, Neuroscience, Algorithm, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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