1 citations · 1 across the 1 of their papers we have counts for
5 papers
A cross-species neural foundation model for end-to-end speech decoding
Yizi Zhang, Linyang He, Chaofei Fan +9
Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…
SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
Trung Le, Hao Fang, Jingyuan Li +5
Intracortical Brain-Computer Interfaces (iBCI) aim to decode behavior from neural population activity, enabling individuals with motor impairments to regain motor functions and com…
SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
Mingfei Chen, Zijun Cui, Xiulong Liu +4
3D spatial reasoning in dynamic, audio-visual environments is a cornerstone of human cognition yet remains largely unexplored by existing Audio-Visual Large Language Models (AV-LLM…
Brain-to-Text Benchmark '24: Lessons Learned
Francis R. Willett, Jingyuan Li, Trung Le +13
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the abili…
Brain-to-Text Decoding with Context-Aware Neural Representations and Large Language Models
Jingyuan Li, Trung Le, Chaofei Fan +2
Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused…