CAT-Net: A Cross-Attention Tone Network for Cross-Subject EEG-EMG Fusion Tone Decoding

The article introduces STAMP, a Spatial-Temporal Adapter with Multi-Head Pooling, designed for time series foundation models (TSFMs) specifically applied to electroencephalography (EEG) data. STAMP utilizes univariate embeddings from general TSFMs to model the spatial-temporal characteristics of EEG data effectively. The study demonstrates that STAMP achieves performance comparable to state-of-the-art EEG-specific foundation models (EEGFMs) across eight benchmark datasets used for classification tasks.

The article discusses EMOD, a new framework for emotion recognition from EEG signals, which addresses the limitations of existing deep learning models. These models often struggle with generalization across different datasets due to varying annotation schemes and data formats. EMOD utilizes Valence-Arousal (V-A) Guided Contrastive Learning to create transferable representations from heterogeneous datasets, projecting emotion labels into a unified V-A space and employing a soft-weighted supervised contrastive loss to enhance performance.

The article discusses a new adaptive teaching paradigm aimed at improving the decoding of visual features from EEG signals. It highlights the inherent asymmetry between visual and brain modalities, characterized by a Fidelity Gap and a Semantic Gap. The proposed method allows the visual modality to adjust its knowledge structure to better align with the EEG modality, achieving a top-1 accuracy of 60.2%, which is a 9.8% improvement over previous methods.