2 citations · 4 across the 7 of their papers we have counts for
7 papers
One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models
Changli Tang, Shurui Li, Junliang Wang +8
Deciphering brain function through non-invasive recordings requires synthesizing complementary high-frequency electromagnetic (EEG/MEG) and low-frequency metabolic (fMRI) signals.…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Fengyu Yang, Chao Feng, Daniel Wang +8
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like e…
Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old
Lingbin Bian, Nizhuan Wang, Yuanning Li +5
The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. Traditional methods for estimat…
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs
Linyang He, Peili Chen, Ercong Nie +2
Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in…
Do self-supervised speech and language models extract similar representations as human brain?
Peili Chen, Linyang He, Li Fu +3
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given the…
Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction
Jiawei Li, Chunxu Guo, Li Fu +3
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural…