Generative Visual Grounding creates visual proxy images from EEG to enhance MLLM understanding of brain signals beyond text-only alignment.
Eeg-gpt: exploring capabilities of large language models for eeg classification and interpretation
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LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.
citing papers explorer
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Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates visual proxy images from EEG to enhance MLLM understanding of brain signals beyond text-only alignment.
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LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
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Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.