1 citations · 2 across the 11 of their papers we have counts for
11 papers
Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification
Yun Kwan Kim, Minji Lee, Kunwook Jo +2
Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms…
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…
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.…
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface
Byeong-Hoo Lee, Jeong-Hyun Cho, Byoung-Hee Kwon +1
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performan…
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Keon-Woo Lee, Dae-Hyeok Lee, Sung-Jin Kim +1
Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface ha…
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
Serkan Musellim, Dong-Kyun Han, Ji-Hoon Jeong +1
Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitate…
Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals
Jeong-Hyun Cho, Byoung-Hee Kwon, Byeong-Hoo Lee +1
Brain-computer interface (BCI) is a practical pathway to interpret users' intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals.…
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification
Ji-Seon Bang, Seong-Whan Lee
Deep learning frameworks have become increasingly popular in brain computer interface (BCI) study thanks to their outstanding performance. However, in terms of the classification m…
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…
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…
Decoding Movement Imagination and Execution from EEG Signals using BCI-Transfer Learning Method based on Relation Network
D. -Y. Lee, J. -H. Jeong, K. -H. Shim +1
A brain-computer interface (BCI) is used not only to control external devices for healthy people but also to rehabilitate motor functions for motor-disabled patients. Decoding move…