no code implementations • 10 Dec 2023 • Seo-Hyun Lee, Young-Eun Lee, Soowon Kim, Byung-Kwan Ko, Jun-Young Kim, Seong-Whan Lee
Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis.
no code implementations • 14 Nov 2023 • Soowon Kim, Seo-Hyun Lee, Young-Eun Lee, Ji-Won Lee, Ji-Ha Park, Seong-Whan Lee
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.
no code implementations • 14 Nov 2023 • Young-Eun Lee, Seo-Hyun Lee, Soowon Kim, Jung-Sun Lee, Deok-Seon Kim, Seong-Whan Lee
Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performance for BCI.
1 code implementation • 26 Jul 2023 • Soowon Kim, Young-Eun Lee, Seo-Hyun Lee, Seong-Whan Lee
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.
no code implementations • 19 Jan 2023 • Soowon Kim, Ji-Won Lee, Young-Eun Lee, Seo-Hyun Lee
A brain-computer interface system can be implemented using electroencephalogram signals because it poses more less clinical risk and can be acquired using portable instruments.
1 code implementation • 2 Jan 2023 • Young-Eun Lee, Seo-Hyun Lee, Sang-Ho Kim, Seong-Whan Lee
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.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 3 Mar 2021 • Young-Eun Lee, Seong-Whan Lee
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment.
no code implementations • 18 May 2020 • Young-Eun Lee, Minji Lee, Seong-Whan Lee
As a result, the reconstructed signals had important components such as N200 and P300 similar to ERP during standing.