Brain data is worth a variable number of task samples depending on task-brain alignment, noise levels, and latent dimension, with conditions under which it also improves robustness to test distribution shift.
Scaling laws for decoding images from brain activity
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.
Augmenting limited fMRI datasets with synthetic responses from TRIBE v2 improves brain-to-image decoding accuracy and can yield above-chance performance using only synthetic data.
citing papers explorer
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How Much is Brain Data Worth for Machine Learning?
Brain data is worth a variable number of task samples depending on task-brain alignment, noise levels, and latent dimension, with conditions under which it also improves robustness to test distribution shift.
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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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A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
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Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
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DANCE: Detect and Classify Events in EEG
DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.
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Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Augmenting limited fMRI datasets with synthetic responses from TRIBE v2 improves brain-to-image decoding accuracy and can yield above-chance performance using only synthetic data.