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UNWIND is introduced, a facial-video framework for stress detection that analyzes a complete recording as a single model input, eliminating the need for temporal windowing or external segmentation and showing that complete-recording inference can be performed within a single unified model.
The results show that alternative visual forms of the same EDA signal can provide useful inductive biases for stress detection, and that a compact selection of complementary representations can be more effective than the raw waveform alone.
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.
The findings demonstrate the feasibility of processing structurally different neural and visual signals within a compact unified architecture while highlighting the importance of controlling duration-related cues in affective datasets.
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