Bo Chai

Jiahe Meng, Weiming Zeng, Yueyang Li et al.

Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--language spaces, making direct cross-modal alignment unstable. To address this, we propose STAMBRIDGE, a versatile two-stage framework that sequentially tackles feature conditioning and cross-modal alignment. First, we introduce a Spectral-Temporal Amplitude-aware Modulation (STAM) to extract well-conditioned EEG representations. By replacing hard frequency masking with amplitude-derived soft channel weighting and multi-scale temporal convolutions, STAM explicitly preserves frequency-aware transients while reducing the risk of time-domain ringing artifacts. Building upon these robust neural features, we further introduce a model-agnostic Mid-Feature Semantic Bridge (MFSB) that constructs a regularized intermediate space through directed cross-modal interactions, enabling staged distillation and more stable semantic alignment. Experiments on the THINGS-EEG benchmark show competitive 200-way zero-shot retrieval performance, with 34.50\% Top-1 and 65.95\% Top-5 accuracy. In addition, embeddings learned by STAMBRIDGE produce semantically coherent image reconstructions with a diffusion model, demonstrating robust EEG-to-vision semantic alignment. The code is available at: https://github.com/thabeatmjh/STAMBRIDGE.

Shengyu Gong, Yueyang Li, Zijian Kang et al.

The accurate and efficient recognition of emotional states in oneself and others is critical, as impairments in this ability can lead to significant psychosocial difficulties. While electroencephalography (EEG) offers a powerful tool for emotion detection, current EEG-based emotion recognition (EER) methods face key limitations: insufficient model stability, limited accuracy in processing high-dimensional nonlinear EEG signals, and poor robustness against intra-subject variability and signal noise. To address these challenges, we introduce LEL (Lipschitz continuity-constrained Ensemble Learning), a novel framework that enhances EEG-based emotion recognition. By integrating Lipschitz continuity constraints, LEL ensures greater model stability and improves generalization, thereby reducing sensitivity to signal variability and noise while significantly boosting the model's overall accuracy and robustness. Its ensemble learning strategy optimizes overall performance by fusing decisions from multiple classifiers to reduce single-model bias and variance. Experimental results on three public benchmark datasets (EAV, FACED and SEED) demonstrated the LEL's state-of-the-art performance, achieving average recognition accuracies of 76.43%, 83.00% and 87.22%, respectively. The official implementation codes are released at https://github.com/NZWANG/LEL.