Medical Xpress February 28, 2025
Yale University, Dartmouth College, and the University of Cambridge researchers have developed MindLLM, a subject-agnostic model for decoding functional magnetic resonance imaging (fMRI) signals into text.
Integrating a neuroscience-informed attention mechanism with a large language model (LLM), the model outperforms existing approaches with a 12.0% improvement in downstream tasks, a 16.4% increase in unseen subject generalization, and a 25.0% boost in novel task adaptation compared to prior models like UMBRAE, BrainChat, and UniBrain.
Decoding brain activity into natural language has significant implications for neuroscience and brain-computer interface applications. Previous attempts have faced challenges in predictive performance, limited task variety, and poor generalization across subjects. Existing approaches often require subject-specific parameters, limiting their ability to generalize across individuals.
In the study...
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