用对比学习提升脑电情绪识别跨域性能
Cross-domain EEG-based Emotion Recognition with Contrastive Learning
- 将脑电信号与文本匹配，利用对比学习建模情绪
- 跨被试准确率最高达88.69%，跨时间达77.54%
- 适合关注脑机接口与情感计算的科研人员
基于脑电（EEG）的情绪识别对情感计算至关重要，但面临特征利用不足和跨域泛化差的挑战。本文提出EmotionCLIP，将情绪识别重构为EEG-文本匹配任务，基于CLIP框架实现。设计专用骨干网络SST-LegoViT，融合多尺度卷积与Transformer模块，有效捕捉空间、频谱和时序特征。在SEED和SEED-IV数据集上的实验表明，跨被试准确率分别达到88.69%和73.50%，跨时间准确率分别为88.46%和77.54%，优于现有模型。结果验证了多模态对比学习在鲁棒脑电情绪识别中的有效性。代码已开源：https://github.com/Departure2021/EmotionCLIP。
原文摘要 · Abstract (English)
Electroencephalogram (EEG)-based emotion recognition is vital for affective computing but faces challenges in feature utilization and cross-domain generalization. This work introduces EmotionCLIP, which reformulates recognition as an EEG-text matching task within the CLIP framework. A tailored backbone, SST-LegoViT, captures spatial, spectral, and temporal features using multi-scale convolution and Transformer modules. Experiments on SEED and SEED-IV datasets show superior cross-subject accuracies of 88.69\% and 73.50\%, and cross-time accuracies of 88.46\% and 77.54\%, outperforming existing models. Results demonstrate the effectiveness of multimodal contrastive learning for robust EEG emotion recognition. The code is available at https://github.com/Departure2021/EmotionCLIP.
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