Decoding attempted speech from neural activity offers a promising avenue for restoring
communication abilities in individuals with speech impairments. Previous studies have …

Decoding linguistic information from non-invasive brain signals using EEG has gained
increasing research attention due to its vast applicational potential. Recently, a number of …

This paper introduces NeuGPT, a groundbreaking multi-modal language generation model
designed to harmonize the fragmented landscape of neural recording research …

Decoding speech from brain signals has been a long-standing challenge in neuroscience
and brain-computer interface research. While significant progress has been made in English …

Recent advances in decoding language from brain signals (EEG and MEG) have been
significantly driven by pre-trained language models, leading to remarkable progress on …

Recent advancements in humanoid robotics, including the integration of hierarchical
reinforcement learning-based control and the utilization of LLM planning, have significantly …

Deciphering language from brain activity is a crucial task in brain-computer interface (BCI)
research. Non-invasive cerebral signaling techniques including electroencephalography …

This work critically analyzes existing models for open-vocabulary EEG-to-Text translation.
We identify a crucial limitation: previous studies often employed implicit teacher-forcing …