MindAlign decodes inner speech from fMRI via subject-specific neural-semantic alignment into a multimodal space followed by prompting of a frozen LM, outperforming baselines and generalizing across subjects.
Open-vocabulary auditory neural decoding using fMRI-prompted LLM
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
EmoMind is the first end-to-end pipeline that decodes continuous affective captions from fMRI by combining brain-decoded visual features with a 34D emotion vector and classifier-free guidance to balance semantic fidelity and affective expressivity.
SABER integrates LLM semantics into brain networks via global self-attention and multi-scale hypergraphs with decision-level alignment, claiming SOTA performance, stability, and interpretability on ABIDE and ADHD-200.
citing papers explorer
-
MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data
MindAlign decodes inner speech from fMRI via subject-specific neural-semantic alignment into a multimodal space followed by prompting of a frozen LM, outperforming baselines and generalizing across subjects.
-
EmoMind: Decoding Affective Captions from Human Brain fMRI
EmoMind is the first end-to-end pipeline that decodes continuous affective captions from fMRI by combining brain-decoded visual features with a 34D emotion vector and classifier-free guidance to balance semantic fidelity and affective expressivity.
-
SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs
SABER integrates LLM semantics into brain networks via global self-attention and multi-scale hypergraphs with decision-level alignment, claiming SOTA performance, stability, and interpretability on ABIDE and ADHD-200.