no code implementations • 13 Dec 2022 • Sung-Jin Kim, Dae-Hyeok Lee, Yeon-Woo Choi
To solve these issues, we proposed a novel data augmentation method, CropCat.
no code implementations • 13 Dec 2022 • Dae-Hyeok Lee, Sung-Jin Kim, Yeon-Woo Choi
We proposed a model for classifying distraction levels.
no code implementations • 15 Apr 2022 • Keon-Woo Lee, Dae-Hyeok Lee, Sung-Jin Kim, Seong-Whan Lee
In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese.
no code implementations • 15 Jul 2021 • Dae-Hyeok Lee, Sung-Jin Kim, Seong-Whan Lee
In addition, when comparing the performance between w/o and w/ EEG features of overt speech, there was a performance improvement of 7. 42% when including EEG features of overt speech.
no code implementations • 25 Jun 2021 • Hyung-Ju Ahn, Dae-Hyeok Lee, Ji-Hoon Jeong, Seong-Whan Lee
Moreover, our proposed TINN showed the highest accuracy of 0. 93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).
no code implementations • 8 Jun 2021 • Dae-Hyeok Lee, Dong-Kyun Han, Sung-Jin Kim, Ji-Hoon Jeong, Seong-Whan Lee
Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain.
no code implementations • 3 Feb 2020 • Ji-Hoon Jeong, Dae-Hyeok Lee, Hyung-Ju Ahn, Seong-Whan Lee
Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.