FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
A large finer-grained affective computing eeg dataset
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ADaCoRe enables memory-bounded UICL for EEG by compressing and reconstructing signals while preserving key morphologies, outperforming baselines with gains of at least +2.7 and +15.3 ACC on ISRUC and FACED datasets.
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
citing papers explorer
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Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
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Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning
ADaCoRe enables memory-bounded UICL for EEG by compressing and reconstructing signals while preserving key morphologies, outperforming baselines with gains of at least +2.7 and +15.3 ACC on ISRUC and FACED datasets.
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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.