Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Wonjun Ko"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learnin
Externí odkaz:
https://doaj.org/article/8cf847474d4a4e9c8e563fddc033b9ed
Publikováno v:
NeuroImage, Vol 236, Iss , Pp 118048- (2021)
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grain
Externí odkaz:
https://doaj.org/article/3ad8a1b10e5d47dca5f0f71fa9c72bae
Autor:
Byoung-Kyong Min, Hyun-Seok Kim, Wonjun Ko, Min-Hee Ahn, Heung-Il Suk, Dimitrios Pantazis, Robert T. Knight
Publikováno v:
NeuroImage, Vol 237, Iss , Pp 118165- (2021)
The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized lin
Externí odkaz:
https://doaj.org/article/0be81a7fa9704f5bbd248041d86ab990
Publikováno v:
Frontiers in Human Neuroscience, Vol 15 (2021)
Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG
Externí odkaz:
https://doaj.org/article/52dcf19fccfd4e9698841b76a2c0aefa
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:1873-1882
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7fc601e812ae617ecd889ebba35c7be
https://doi.org/10.1109/tii.2020.3044310
https://doi.org/10.1109/tii.2020.3044310
Autor:
Wonjun Ko, Heung-Il Suk
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c52f2861f676156cf12514eaed4e8a19
https://doi.org/10.1145/3511808.3557589
https://doi.org/10.1145/3511808.3557589
Publikováno v:
IEEE Computational Intelligence Magazine. 16:31-45
Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal elect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e04d666dea7bd3fb460930fb8668fd16
https://doi.org/10.1109/mci.2021.3061875
https://doi.org/10.1109/mci.2021.3061875
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48b87876ca5210ce45f25bda3af571b3
https://doi.org/10.1109/icassp43922.2022.9746353
https://doi.org/10.1109/icassp43922.2022.9746353
Publikováno v:
IEEE transactions on medical imaging. 41(9)
Imaging genetics, one of the foremost emerging topics in the medical imaging field, analyzes the inherent relations between neuroimaging and genetic data. As deep learning has gained widespread acceptance in many applications, pioneering studies empl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a946ea84128f1e286d8dc5db1fb12f2
https://pubmed.ncbi.nlm.nih.gov/35344489
https://pubmed.ncbi.nlm.nih.gov/35344489
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning architectures for sleep staging. However, two key challenges hinder the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a3bf451918080ea32271d4970608819
http://arxiv.org/abs/2203.12590
http://arxiv.org/abs/2203.12590
