Enhancing EEG Foundation Models via Dual-Branch Self-Distillation With Bi-Pretext Tasks
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 3214379286 | ||
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| 020 | |a 9798315778073 | ||
| 035 | |a 3214379286 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Hung, Wei-Lun Allen | |
| 245 | 1 | |a Enhancing EEG Foundation Models via Dual-Branch Self-Distillation With Bi-Pretext Tasks | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a We present a dual-branch self-supervised learning framework for EEG representation learning, combining masked reconstruction and clustering-based objectives. Evaluated across five diverse downstream tasks, our method achieves state-of-the-art performance under both linear probing and fine-tuning protocols. Ablation and visualization analyses confirm the robustness and transferability of the learned features. Our approach offers a promising foundation for future advances in general-purpose EEG analysis. | |
| 653 | |a Computer science | ||
| 653 | |a Computer engineering | ||
| 653 | |a Information technology | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3214379286/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3214379286/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |