EEGDataAugmentation Method Based on the Gaussian Mixture Model Article Swipe
Chuncheng Liao
,
Shiyu Zhao
,
Xiangcun Wang
,
Jiacai Zhang
,
Yongzhong Liao
,
Xia Wu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.1667.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.1667.v1
Traditional methods of electroencephalograms(EEG) data augmentation, such as segmentation-reassembly and noise mixing, suffer from data distortion that can alter the original temporal and spatial feature distributions of the brain signals. Deep learning-based methods for generating augmentation EEG data, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have shown promising performance but require a large number of comparative learning samples for model training. To address these issues, this paper introduces an EEG data augmentation method based on Gaussian Mixture Model microstates, which retains the spatiotemporal dynamic features of the EEG signals in the generated data. The method first performs Gaussian mixture clustering on data samples of the same class, using the product of the probability coefficients and weight matrices of each Gaussian model as corresponding microstate features. Next, it randomly selects two EEG data samples of the same type, analyzes the similarity of the main components of the microstate features, and swaps the similar main components to form new Gaussian mixture model features. Finally, new data is generated according to the Gaussian mixture model using the respective model probabilities, weights, means, and variances. Experimental results on publicly available datasets demonstrate that the proposed method effectively characterizes the original data's spatiotemporal and microstate features, improving the accuracy of subject task classification.
Related Topics
Concepts
Mixture model
Computer science
Pattern recognition (psychology)
Artificial intelligence
Ministate
Gaussian
Cluster analysis
Gaussian network model
Generative model
Feature (linguistics)
Similarity (geometry)
Electroencephalography
Generative grammar
Image (mathematics)
Psychology
Linguistics
Physics
Quantum mechanics
Philosophy
Psychiatry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.1667.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406744356
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4406744356Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202501.1667.v1Digital Object Identifier
- Title
-
EEGDataAugmentation Method Based on the Gaussian Mixture ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-01-22Full publication date if available
- Authors
-
Chuncheng Liao, Shiyu Zhao, Xiangcun Wang, Jiacai Zhang, Yongzhong Liao, Xia WuList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202501.1667.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202501.1667.v1Direct OA link when available
- Concepts
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Mixture model, Computer science, Pattern recognition (psychology), Artificial intelligence, Ministate, Gaussian, Cluster analysis, Gaussian network model, Generative model, Feature (linguistics), Similarity (geometry), Electroencephalography, Generative grammar, Image (mathematics), Psychology, Linguistics, Physics, Quantum mechanics, Philosophy, PsychiatryTop 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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| abstract_inverted_index.selects | 131 |
| abstract_inverted_index.signals | 91 |
| abstract_inverted_index.similar | 154 |
| abstract_inverted_index.spatial | 23 |
| abstract_inverted_index.subject | 208 |
| abstract_inverted_index.Finally, | 164 |
| abstract_inverted_index.Gaussian | 78, 100, 122, 160, 172 |
| abstract_inverted_index.Networks | 42 |
| abstract_inverted_index.accuracy | 206 |
| abstract_inverted_index.analyzes | 140 |
| abstract_inverted_index.datasets | 189 |
| abstract_inverted_index.features | 87 |
| abstract_inverted_index.learning | 59 |
| abstract_inverted_index.matrices | 119 |
| abstract_inverted_index.original | 20, 198 |
| abstract_inverted_index.performs | 99 |
| abstract_inverted_index.proposed | 193 |
| abstract_inverted_index.publicly | 187 |
| abstract_inverted_index.randomly | 130 |
| abstract_inverted_index.signals. | 29 |
| abstract_inverted_index.temporal | 21 |
| abstract_inverted_index.weights, | 180 |
| abstract_inverted_index.according | 169 |
| abstract_inverted_index.available | 188 |
| abstract_inverted_index.features, | 150, 203 |
| abstract_inverted_index.features. | 127, 163 |
| abstract_inverted_index.generated | 94, 168 |
| abstract_inverted_index.improving | 204 |
| abstract_inverted_index.promising | 50 |
| abstract_inverted_index.training. | 63 |
| abstract_inverted_index.Generative | 40 |
| abstract_inverted_index.clustering | 102 |
| abstract_inverted_index.components | 146, 156 |
| abstract_inverted_index.distortion | 15 |
| abstract_inverted_index.generating | 34 |
| abstract_inverted_index.introduces | 70 |
| abstract_inverted_index.microstate | 126, 149, 202 |
| abstract_inverted_index.respective | 177 |
| abstract_inverted_index.similarity | 142 |
| abstract_inverted_index.variances. | 183 |
| abstract_inverted_index.Adversarial | 41 |
| abstract_inverted_index.Traditional | 0 |
| abstract_inverted_index.Variational | 45 |
| abstract_inverted_index.comparative | 58 |
| abstract_inverted_index.demonstrate | 190 |
| abstract_inverted_index.effectively | 195 |
| abstract_inverted_index.performance | 51 |
| abstract_inverted_index.probability | 115 |
| abstract_inverted_index.Autoencoders | 46 |
| abstract_inverted_index.Experimental | 184 |
| abstract_inverted_index.augmentation | 35, 74 |
| abstract_inverted_index.coefficients | 116 |
| abstract_inverted_index.microstates, | 81 |
| abstract_inverted_index.augmentation, | 5 |
| abstract_inverted_index.characterizes | 196 |
| abstract_inverted_index.corresponding | 125 |
| abstract_inverted_index.distributions | 25 |
| abstract_inverted_index.learning-based | 31 |
| abstract_inverted_index.probabilities, | 179 |
| abstract_inverted_index.spatiotemporal | 85, 200 |
| abstract_inverted_index.classification. | 210 |
| abstract_inverted_index.segmentation-reassembly | 8 |
| abstract_inverted_index.electroencephalograms(EEG) | 3 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.02137518 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |