fmri

NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.

This paper investigates whether Brain Score, a metric comparing language model representations to human fMRI activations during reading, is truly capturing human-like language processing or merely structural similarity. The researchers train language models on diverse natural languages and non-linguistic structured data (genome, Python, nested parentheses), finding that models trained on different languages and even non-linguistic sequences achieve similar Brain Score performance, suggesting the metric may not be sensitive enough to distinguish human-specific processing.

This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.