Enhancing EEG Foundation Models via Dual-Branch Self-Distillation With Bi-Pretext Tasks
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| 出版年: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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| 抄録: | 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. |
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| ISBN: | 9798315778073 |
| ソース: | ProQuest Dissertations & Theses Global |