26 citations · 28 across the 11 of their papers we have counts for
11 papers
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…
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.…
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…
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…
Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running
Young-Eun Lee, Gi-Hwan Shin, Minji Lee +1
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four differen…
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…
Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment
Young-Eun Lee, Seong-Whan Lee
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by m…