A human-centric metaverse enabled by brain-computer interface: A survey

HY Zhu, NQ Hieu, DT Hoang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The growing interest in the Metaverse has generated momentum for members of academia
and industry to innovate toward realizing the Metaverse world. The Metaverse is a unique …

2020 International brain–computer interface competition: A review

JH Jeong, JH Cho, YE Lee, SH Lee, GH Shin… - Frontiers in human …, 2022 - frontiersin.org
The brain-computer interface (BCI) has been investigated as a form of communication tool
between the brain and external devices. BCIs have been extended beyond communication …

EEG-transformer: Self-attention from transformer architecture for decoding EEG of imagined speech

YE Lee, SH Lee - 2022 10th International winter conference on …, 2022 - ieeexplore.ieee.org
Transformers are groundbreaking architectures that have changed a flow of deep learning,
and many high-performance models are develo** based on transformer architectures …

Automatic cardiac arrhythmia classification using residual network combined with long short-term memory

YK Kim, M Lee, HS Song… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Diagnosis and classification of arrhythmia, which is associated with abnormal electrical
activities in the heart, are critical for clinical treatments. Previous studies focused on the …

Towards voice reconstruction from EEG during imagined speech

YE Lee, SH Lee, SH Kim, SW Lee - … of the AAAI Conference on Artificial …, 2023 - ojs.aaai.org
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 …

Multi-scale neural network for EEG representation learning in BCI

W Ko, E Jeon, S Jeong, HI Suk - IEEE Computational …, 2021 - ieeexplore.ieee.org
Recent advances in deep learning have had a methodological and practical impact on brain-
computer interface (BCI) research. Among the various deep network architectures …

Deep-learning-based BCI for automatic imagined speech recognition using SPWVD

A Kamble, PH Ghare, V Kumar - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The electroencephalogram (EEG)-based brain–computer interface (BCI) has potential
applications in neuroscience and rehabilitation. It benefits a person with neurological …

Real-time deep neurolinguistic learning enhances noninvasive neural language decoding for brain–machine interaction

JH Jeong, JH Cho, BH Lee… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG)-based brain–machine interface (BMI) has been utilized to
help patients regain motor function and has recently been validated for its use in healthy …

Understanding the brain with attention: A survey of transformers in brain sciences

C Chen, H Wang, Y Chen, Z Yin, X Yang, H Ning… - Brain‐X, 2023 - Wiley Online Library
Owing to their superior capabilities and advanced achievements, Transformers have
gradually attracted attention with regard to understanding complex brain processing …

Decoding imagined speech based on deep metric learning for intuitive BCI communication

DY Lee, M Lee, SW Lee - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Imagined speech is a highly promising paradigm due to its intuitive application and
multiclass scalability in the field of brain-computer interfaces. However, optimal feature …