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Enhancing EEG Foundation Models via Dual-Branch Self-Distillation With Bi-Pretext Tasks

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Veröffentlicht in:ProQuest Dissertations and Theses (2025)
1. Verfasser: Hung, Wei-Lun Allen
Veröffentlicht:
ProQuest Dissertations & Theses
Schlagworte:
Computer science
Computer engineering
Information technology
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Abstract: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.
ISBN:9798315778073
Quelle:ProQuest Dissertations & Theses Global