1 citations · 2 across the 7 of their papers we have counts for
7 papers
Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces
Seo-Hyun Lee, Ji-Ha Park, Deok-Seon Kim
Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to rec…
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…
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…
Brain-Driven Representation Learning Based on Diffusion Model
Soowon Kim, Seo-Hyun Lee, Young-Eun Lee +3
Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. D…
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…