4 citations · 10 across the 10 of their papers we have counts for
10 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,…
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…
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG
Soowon Kim, Young-Eun Lee, Seo-Hyun Lee +1
Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion pr…
Subject-Independent Classification of Brain Signals using Skip Connections
Soowon Kim, Ji-Won Lee, Young-Eun Lee +1
Untapped potential for new forms of human-to-human communication can be found in the active research field of studies on the decoding of brain signals of human speech. A brain-comp…
Towards Voice Reconstruction from EEG during Imagined Speech
Young-Eun Lee, Seo-Hyun Lee, Sang-Ho Kim +1
Translating imagined speech from human brain activity into voice is a challenging and absorbing research issue that can provide new means of human communication via brain signals.…
EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech
Young-Eun Lee, Seo-Hyun Lee
Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transfo…
Voice of Your Brain: Cognitive Representations of Imagined Speech,Overt Speech, and Speech Perception Based on EEG
Seo-Hyun Lee, Young-Eun Lee, Seong-Whan Lee
Every people has their own voice, likewise, brain signals dis-play distinct neural representations for each individual. Al-though recent studies have revealed the robustness of spe…
Functional Connectivity of Imagined Speech and Visual Imagery based on Spectral Dynamics
Seo-Hyun Lee, Minji Lee, Seong-Whan Lee
Recent advances in brain-computer interface technology have shown the potential of imagined speech and visual imagery as a robust paradigm for intuitive brain-computer interface co…