NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.
Brainwave: A brain signal foundation model for clinical applications.arXiv preprint arXiv:2402.10251
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Neuroprobe is a new suite of decoding tasks on the BrainTreebank iEEG dataset for evaluating multi-modal language processing in the brain during naturalistic movie viewing.
SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
citing papers explorer
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NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.
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Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
Neuroprobe is a new suite of decoding tasks on the BrainTreebank iEEG dataset for evaluating multi-modal language processing in the brain during naturalistic movie viewing.
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SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
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Wearable AI in the Era of Large Sensor Models
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
- Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs