EMOD: A Unified EEG Emotion Representation Framework Leveraging V-A Guided Contrastive Learning

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 study presents CAT-Net, a novel cross-subject multimodal brain-computer interface (BCI) decoding framework that integrates electroencephalography (EEG) and electromyography (EMG) signals to classify four Mandarin tones. This approach addresses the challenges of tonal variations in Mandarin, which can alter meanings despite identical phonemes. The framework demonstrates strong performance, achieving classification accuracies of 87.83% for audible speech and 88.08% for silent speech across 4800 EEG and 4800 EMG trials with 10 participants.

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.