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A unified, modality-agnostic, hierarchical Transformer-based architecture to process heterogeneous biosignal modalities within a single model is developed and EEG is the strongest single modality, ranking highest in IQ, GAME, and the pooled ALL setting.
This study introduces a multi-scale temporal framework for EEG-based emotion recognition that is decomposed into windows of one or several durations, processed by a shared attention-based encoder, and integrated through a dynamic fusion module that assigns sample-specific weights across temporal scales.
A unified tokenization framework for heterogeneous 3D modalities in pain recognition that provides a single processing pipeline across behavioral and brain-activity 3D data, without requiring separate architectures for each modality or handcrafted inductive biases is introduced.
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