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2020 – today
- 2024
[j6]Kyungdo Kim
, Kwangsoo Kim, Seung-Bo Lee:
PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain-computer interface. Neurocomputing 593: 127805 (2024)- [j5]Doyun Kwon, Young Mi Jung, Hyung-Chul Lee, Tae Kyong Kim, Kwangsoo Kim, Garam Lee, Dokyoon Kim, Seung-Bo Lee, Seung Mi Lee:
Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data. J. Biomed. Informatics 156: 104680 (2024) - 2023
- [j4]Jin-Yeong Kang, Ye Seul Bae, Eui Kyu Chie, Seung-Bo Lee:
Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. Sensors 23(23): 9597 (2023) - 2022
- [j3]Jaesung Yoo, Ilhan Yoo, Ina Youn, Sung-Min Kim, Ri Yu, Kwangsoo Kim, Keewon Kim, Seung-Bo Lee:
Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability. Comput. Methods Programs Biomed. 226: 107079 (2022) - 2020
- [c7]Seung-Bo Lee, Minkyung Jung, Hakseung Kim, Seong-Whan Lee, Dong-Joo Kim:
Complex Motor Imagery-based Brain-Computer Interface System: A Comparison Between Different Classifiers. SMC 2020: 2496-2501
2010 – 2019
- 2019
- [j2]Seung-Bo Lee, Hyun-Ji Kim, Hakseung Kim, Ji-Hoon Jeong, Seong-Whan Lee, Dong-Joo Kim:
Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification. Inf. Sci. 502: 190-200 (2019) - [c6]Young-Tak Kim, Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, Seong-Whan Lee, Dong-Joo Kim:
Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping. BCI 2019: 1-4 - [c5]Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, In-Nea Wang, Seong-Whan Lee, Dong-Joo Kim:
Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification. BCI 2019: 1-4 - [c4]Young-Tak Kim, Seho Lee, Hakseung Kim, Seung-Bo Lee, Seong-Whan Lee, Dong-Joo Kim:
Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation. SMC 2019: 2139-2143 - [c3]Seung-Bo Lee, Hakseung Kim, Seho Lee, Hyun-Ji Kim, Seong-Whan Lee, Dong-Joo Kim:
Classification of the Motion Artifacts in Near-infrared Spectroscopy Based on Wavelet Statistical Feature. SMC 2019: 2144-2148 - 2018
- [j1]Yunsik Son, Seung-Bo Lee, Hakseung Kim, Eun-Suk Song, Hyub Huh, Marek Czosnyka, Dong-Joo Kim:
Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms. Inf. Sci. 456: 145-158 (2018) - [c2]Seung-Bo Lee, Eun-Suk Song, Hakseung Kim, Dong-Joo Kim:
Robust arterial blood pressure onset detection method from signal artifacts. BCI 2018: 1-3 - 2017
- [c1]Seung-Bo Lee, Eun-Jin Jeong, Yunsik Son, Dong-Joo Kim:
Classification of computed tomography scanner manufacturer using support vector machine. BCI 2017: 85-87
Coauthor Index
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