EMPT: a sparsity Transformer for EEG-based motor imagery recognition Article Swipe
Ming Liu
,
Yanbing Liu
,
Weiyou Shi
,
Yitai Lou
,
Yuan Sun
,
Meng Qi
,
Dezheng Wang
,
Fangzhou Xu
,
Yang Zhang
,
Lei Zhang
,
Jiancai Leng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2024.1366294
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2024.1366294
Introduction Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm. Methods We propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer. Results EMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods. Discussion The MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2024.1366294
- https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1366294/pdf
- OA Status
- gold
- Cited By
- 7
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394928596
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- OpenAlex ID
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https://openalex.org/W4394928596Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2024.1366294Digital Object Identifier
- Title
-
EMPT: a sparsity Transformer for EEG-based motor imagery recognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-04-18Full publication date if available
- Authors
-
Ming Liu, Yanbing Liu, Weiyou Shi, Yitai Lou, Yuan Sun, Meng Qi, Dezheng Wang, Fangzhou Xu, Yang Zhang, Lei Zhang, Jiancai LengList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2024.1366294Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1366294/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1366294/pdfDirect OA link when available
- Concepts
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Motor imagery, Electroencephalography, Transformer, Computer science, Artificial intelligence, Speech recognition, Pattern recognition (psychology), Psychology, Neuroscience, Engineering, Brain–computer interface, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.introduced | 212 |
| abstract_inverted_index.mechanism, | 226 |
| abstract_inverted_index.Transformer | 1, 62, 215 |
| abstract_inverted_index.comparative | 184 |
| abstract_inverted_index.experiments | 185, 206 |
| abstract_inverted_index.introducing | 219 |
| abstract_inverted_index.s-transform | 110 |
| abstract_inverted_index.transformer | 129 |
| abstract_inverted_index.Introduction | 0 |
| abstract_inverted_index.experiments. | 204 |
| abstract_inverted_index.performance. | 12 |
| abstract_inverted_index.applicability | 230 |
| abstract_inverted_index.visualisation | 203 |
| abstract_inverted_index.Self-attention | 77, 196 |
| abstract_inverted_index.self-attention | 14, 33 |
| abstract_inverted_index.reconstruction, | 134 |
| abstract_inverted_index.kullback-leibler | 222 |
| abstract_inverted_index.state-of-the-art | 188 |
| abstract_inverted_index.MoE-Prob-Transformer | 101 |
| abstract_inverted_index.electroencephalography | 27 |
| abstract_inverted_index.time-frequency-spatial | 82, 117 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100433899, https://openalex.org/A5079311625, https://openalex.org/A5117634646 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I152269853, https://openalex.org/I4210130230, https://openalex.org/I4210142748, https://openalex.org/I4210145099 |
| citation_normalized_percentile.value | 0.91886436 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |