Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models Article Swipe
Mingzhi Chen
,
Yiyu Gui
,
Yuqi Su
,
Yuesheng Zhu
,
Guibo Luo
,
Yuchao Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20253
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.20253
Electroencephalogram (EEG) classification has been widely used in various medical and engineering applications, where it is important for understanding brain function, diagnosing diseases, and assessing mental health conditions. However, the scarcity of EEG data severely restricts the performance of EEG classification networks, and generative model-based data augmentation methods have emerged as potential solutions to overcome this challenge. There are two problems with existing methods: (1) The quality of the generated EEG signals is not high; (2) The enhancement of EEG classification networks is not effective. In this paper, we propose a Transformer-based denoising diffusion probabilistic model and a generated data-based augmentation method to address the above two problems. For the characteristics of EEG signals, we propose a constant-factor scaling method to preprocess the signals, which reduces the loss of information. We incorporated Multi-Scale Convolution and Dynamic Fourier Spectrum Information modules into the model, improving the stability of the training process and the quality of the generated data. The proposed augmentation method randomly reassemble the generated data with original data in the time-domain to obtain vicinal data, which improves the model performance by minimizing the empirical risk and the vicinal risk. We verify the proposed augmentation method on four EEG datasets for four tasks and observe significant accuracy performance improvements: 14.00% on the Bonn dataset; 6.38% on the SleepEDF-20 dataset; 9.42% on the FACED dataset; 2.5% on the Shu dataset. We will make the code of our method publicly accessible soon.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.20253
- https://arxiv.org/pdf/2407.20253
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401202691
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- OpenAlex ID
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https://openalex.org/W4401202691Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.20253Digital Object Identifier
- Title
-
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-20Full publication date if available
- Authors
-
Mingzhi Chen, Yiyu Gui, Yuqi Su, Yuesheng Zhu, Guibo Luo, Yuchao YangList of authors in order
- Landing page
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https://arxiv.org/abs/2407.20253Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.20253Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.20253Direct OA link when available
- Concepts
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Electroencephalography, Computer science, Transformer, Pattern recognition (psychology), Artificial intelligence, Speech recognition, Psychology, Engineering, Electrical engineering, Neuroscience, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data-based | 99 |
| abstract_inverted_index.diagnosing | 21 |
| abstract_inverted_index.effective. | 84 |
| abstract_inverted_index.generative | 43 |
| abstract_inverted_index.minimizing | 182 |
| abstract_inverted_index.preprocess | 121 |
| abstract_inverted_index.reassemble | 162 |
| abstract_inverted_index.Convolution | 133 |
| abstract_inverted_index.Information | 138 |
| abstract_inverted_index.Multi-Scale | 132 |
| abstract_inverted_index.SleepEDF-20 | 217 |
| abstract_inverted_index.conditions. | 27 |
| abstract_inverted_index.engineering | 11 |
| abstract_inverted_index.enhancement | 77 |
| abstract_inverted_index.model-based | 44 |
| abstract_inverted_index.performance | 37, 180, 207 |
| abstract_inverted_index.significant | 205 |
| abstract_inverted_index.time-domain | 171 |
| abstract_inverted_index.augmentation | 46, 100, 159, 194 |
| abstract_inverted_index.incorporated | 131 |
| abstract_inverted_index.information. | 129 |
| abstract_inverted_index.applications, | 12 |
| abstract_inverted_index.improvements: | 208 |
| abstract_inverted_index.probabilistic | 94 |
| abstract_inverted_index.understanding | 18 |
| abstract_inverted_index.classification | 2, 40, 80 |
| abstract_inverted_index.characteristics | 110 |
| abstract_inverted_index.constant-factor | 117 |
| abstract_inverted_index.Transformer-based | 91 |
| abstract_inverted_index.Electroencephalogram | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |