Transformer-based Spatial-Temporal Feature Learning for EEG Decoding Article Swipe
Yonghao Song
,
Xueyu Jia
,
Lie Yang
,
Longhan Xie
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.11170
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.11170
At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most crucial step is to slice the data in the time dimension for attention transforming, and finally obtain a highly distinguishable representation. At this time, global averaging pooling and a simple fully-connected layer are used to classify different categories of EEG data. Experiments on two public datasets indicate that the strategy of attention transforming effectively utilizes spatial and temporal features. And we have reached the level of the state-of-the-art in multi-classification of EEG, with fewer parameters. As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field. It has good potential to promote the practicality of brain-computer interface (BCI). The source code can be found at: \textit{https://github.com/anranknight/EEG-Transformer}.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.11170
- https://arxiv.org/pdf/2106.11170
- OA Status
- green
- Cited By
- 93
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3173912422
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- OpenAlex ID
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https://openalex.org/W3173912422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2106.11170Digital Object Identifier
- Title
-
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-11Full publication date if available
- Authors
-
Yonghao Song, Xueyu Jia, Lie Yang, Longhan XieList of authors in order
- Landing page
-
https://arxiv.org/abs/2106.11170Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2106.11170Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2106.11170Direct OA link when available
- Concepts
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Decoding methods, Transformer, Computer science, Electroencephalography, Feature (linguistics), Pattern recognition (psychology), Artificial intelligence, Speech recognition, Psychology, Engineering, Electrical engineering, Neuroscience, Telecommunications, Voltage, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
93Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 33, 2023: 32, 2022: 14, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.most | 85 |
| abstract_inverted_index.some | 5 |
| abstract_inverted_index.step | 87 |
| abstract_inverted_index.that | 48, 75, 133, 171 |
| abstract_inverted_index.this | 39, 108, 186 |
| abstract_inverted_index.time | 95, 170 |
| abstract_inverted_index.used | 119 |
| abstract_inverted_index.with | 33, 158 |
| abstract_inverted_index.(EEG) | 15 |
| abstract_inverted_index.apply | 67 |
| abstract_inverted_index.based | 7, 177 |
| abstract_inverted_index.data. | 126 |
| abstract_inverted_index.fewer | 159 |
| abstract_inverted_index.first | 169 |
| abstract_inverted_index.found | 205 |
| abstract_inverted_index.know, | 165 |
| abstract_inverted_index.layer | 117 |
| abstract_inverted_index.level | 150 |
| abstract_inverted_index.model | 77 |
| abstract_inverted_index.novel | 44 |
| abstract_inverted_index.slice | 90 |
| abstract_inverted_index.then, | 65 |
| abstract_inverted_index.time, | 109 |
| abstract_inverted_index.which | 25 |
| abstract_inverted_index.(BCI). | 199 |
| abstract_inverted_index.(CNNs) | 12 |
| abstract_inverted_index.common | 30 |
| abstract_inverted_index.field. | 187 |
| abstract_inverted_index.global | 23, 110 |
| abstract_inverted_index.highly | 104 |
| abstract_inverted_index.issue, | 40 |
| abstract_inverted_index.mainly | 49 |
| abstract_inverted_index.method | 47, 176 |
| abstract_inverted_index.neural | 10 |
| abstract_inverted_index.obtain | 102 |
| abstract_inverted_index.people | 2 |
| abstract_inverted_index.public | 130 |
| abstract_inverted_index.relies | 50 |
| abstract_inverted_index.simple | 115 |
| abstract_inverted_index.source | 201 |
| abstract_inverted_index.strong | 35 |
| abstract_inverted_index.crucial | 86 |
| abstract_inverted_index.enhance | 79 |
| abstract_inverted_index.finally | 101 |
| abstract_inverted_index.firstly | 59 |
| abstract_inverted_index.methods | 6 |
| abstract_inverted_index.overall | 36 |
| abstract_inverted_index.pooling | 112 |
| abstract_inverted_index.promote | 193 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.reached | 148 |
| abstract_inverted_index.spatial | 82, 141 |
| abstract_inverted_index.usually | 3 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.adequate | 28 |
| abstract_inverted_index.classify | 121 |
| abstract_inverted_index.complete | 175 |
| abstract_inverted_index.datasets | 131 |
| abstract_inverted_index.decoding | 46 |
| abstract_inverted_index.detailed | 173 |
| abstract_inverted_index.indicate | 132 |
| abstract_inverted_index.networks | 11 |
| abstract_inverted_index.present, | 1 |
| abstract_inverted_index.proposed | 184 |
| abstract_inverted_index.relevant | 81 |
| abstract_inverted_index.strategy | 135 |
| abstract_inverted_index.temporal | 143 |
| abstract_inverted_index.utilizes | 140 |
| abstract_inverted_index.Regarding | 38 |
| abstract_inverted_index.attention | 53, 68, 98, 137 |
| abstract_inverted_index.averaging | 111 |
| abstract_inverted_index.decoding. | 16 |
| abstract_inverted_index.different | 122 |
| abstract_inverted_index.dimension | 73, 96 |
| abstract_inverted_index.features. | 83, 144 |
| abstract_inverted_index.filtered. | 63 |
| abstract_inverted_index.interface | 198 |
| abstract_inverted_index.paradigms | 32 |
| abstract_inverted_index.potential | 191 |
| abstract_inverted_index.spatially | 62 |
| abstract_inverted_index.categories | 123 |
| abstract_inverted_index.mechanism. | 54 |
| abstract_inverted_index.perceiving | 22 |
| abstract_inverted_index.Experiments | 127 |
| abstract_inverted_index.effectively | 139 |
| abstract_inverted_index.limitations | 20 |
| abstract_inverted_index.parameters. | 160 |
| abstract_inverted_index.transformer | 180 |
| abstract_inverted_index.practicality | 195 |
| abstract_inverted_index.preprocessed | 60 |
| abstract_inverted_index.transforming | 69, 138 |
| abstract_inverted_index.convolutional | 9 |
| abstract_inverted_index.dependencies, | 24 |
| abstract_inverted_index.relationship. | 37 |
| abstract_inverted_index.transforming, | 99 |
| abstract_inverted_index.brain-computer | 197 |
| abstract_inverted_index.distinguishable | 105 |
| abstract_inverted_index.feature-channel | 72 |
| abstract_inverted_index.fully-connected | 116 |
| abstract_inverted_index.representation. | 106 |
| abstract_inverted_index.state-of-the-art | 153 |
| abstract_inverted_index.multi-classification | 155 |
| abstract_inverted_index.Electroencephalograph | 14 |
| abstract_inverted_index.\textit{https://github.com/anranknight/EEG-Transformer}. | 207 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |