4 papers
Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models
Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee +1
Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments,…
Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning
Ji-Hoon Heo, Aleksandra Joanna Wisniewska, Seo-Hyun Lee +1
Generalizing across subjects remains challenging in invasive neural recordings because electrode configurations, anatomical structures, and neural signal patterns vary substantiall…
Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding
Ji-Ha Park, Seo-Hyun Lee, Soowon Kim +1
Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. A…
Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface
Ji-Ha Park, Seo-Hyun Lee, Soowon Kim +1
Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an in…