26 citations · 28 across the 13 of their papers we have counts for
13 papers
Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration
Byung-Kwan Ko, Soowon Kim, Seo-Hyun Lee
Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activit…
Confidence-Aware Neural Decoding of Overt Speech from EEG: Toward Robust Brain-Computer Interfaces
Soowon Kim, Byung-Kwan Ko, Seo-Hyun Lee
Non-invasive brain-computer interfaces that decode spoken commands from electroencephalogram must be both accurate and trustworthy. We present a confidence-aware decoding framework…
Imagined Speech State Classification for Robust Brain-Computer Interface
Byung-Kwan Ko, Jun-Young Kim, Seo-Hyun Lee
This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Spe…
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…
EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia
Ha-Na Jo, Young-Seok Kweon, Seo-Hyun Lee
This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences sleep disturbances yet does not full…
Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals
Jung-Sun Lee, Ha-Na Jo, Seo-Hyun Lee
Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer int…
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…
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks
Seo-Hyun Lee, Young-Eun Lee, Soowon Kim +3
Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Ne…
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals
Young-Eun Lee, Seo-Hyun Lee, Soowon Kim +3
Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performan…
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…
Towards Neural Decoding of Imagined Speech based on Spoken Speech
Seo-Hyun Lee, Young-Eun Lee, Soowon Kim +2
Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the parad…
Depth-resolved vascular profile features for artery-vein classification in OCT and OCT angiography of human retina
Tobiloba Adejumo, Tae-Hoon Kim, David Le +3
This study is to characterize reflectance profiles of retinal blood vessels in optical coherence tomography (OCT), and to validate these vascular features to guide artery-vein clas…
Spatio-Temporal Dynamics of Visual Imagery for Intuitive Brain-Computer Interface
Seo-Hyun Lee, Minji Lee, Seong-Whan Lee
Visual imagery is an intuitive brain-computer interface paradigm, referring to the emergence of the visual scene. Despite its convenience, analysis of its intrinsic characteristics…