5 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…
Reconstructing Unseen Sentences from Speech-related Biosignals for Open-vocabulary Neural Communication
Deok-Seon Kim, Seo-Hyun Lee, Kang Yin +1
Brain-to-speech (BTS) systems represent a groundbreaking approach to human communication by enabling the direct transformation of neural activity into linguistic expressions. While…
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…
Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration
Jun-Young Kim, Deok-Seon Kim, Seo-Hyun Lee
In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroe…