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.
Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
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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.
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Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.