Classification of computed tomography scanner manufacturer using support vector machine. - Seung-Bo Lee; Eun-Jin Jeong; Yunsik Son 001; Dong-Joo Kim (BCI, 2017)

Robust arterial blood pressure onset detection method from signal artifacts. - Seung-Bo Lee; Eun-Suk Song; Hakseung Kim; Dong-Joo Kim (BCI, 2018)

Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms. - Yunsik Son 001; Seung-Bo Lee; Hakseung Kim; Eun-Suk Song; Hyub Huh; Marek Czosnyka; Dong-Joo Kim (Inf. Sci., 2018)

Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping. - Young-Tak Kim; Seung-Bo Lee; Hakseung Kim; Ji-Hoon Jeong; Seong-Whan Lee; Dong-Joo Kim (BCI, 2019)

Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification. - Seung-Bo Lee; Hakseung Kim; Ji-Hoon Jeong; In-Nea Wang; Seong-Whan Lee; Dong-Joo Kim (BCI, 2019)

Reduced Burden of Individual Calibration Process in Brain-Computer Interface by Clustering the Subjects based on Brain Activation. - Young-Tak Kim; Seho Lee; Hakseung Kim; Seung-Bo Lee; Seong-Whan Lee; Dong-Joo Kim (SMC, 2019)

Classification of the Motion Artifacts in Near-infrared Spectroscopy Based on Wavelet Statistical Feature. - Seung-Bo Lee; Hakseung Kim; Seho Lee; Hyun-Ji Kim; Seong-Whan Lee; Dong-Joo Kim (SMC, 2019)

Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification. - Seung-Bo Lee; Hyun-Ji Kim; Hakseung Kim; Ji-Hoon Jeong; Seong-Whan Lee; Dong-Joo Kim (Inf. Sci., 2019)

Complex Motor Imagery-based Brain-Computer Interface System: A Comparison Between Different Classifiers. - Seung-Bo Lee; Min-Kyung Jung; Hakseung Kim; Seong-Whan Lee; Dong-Joo Kim (SMC, 2020)

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability. - Jaesung Yoo; Ilhan Yoo; Ina Youn; Sung-Min Kim; Ri Yu; Kwangsoo Kim; Keewon Kim; Seung-Bo Lee (Comput. Methods Programs Biomed., 2022)

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. - Jin-Yeong Kang; Ye Seul Bae; Eui Kyu Chie; Seung-Bo Lee (Sensors, 2023)

PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain-computer interface. - Kyungdo Kim; Kwangsoo Kim; Seung-Bo Lee (Neurocomputing, 2024)

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data. - Doyun Kwon; Young Mi Jung; Hyung-Chul Lee; Tae Kyong Kim; Kwangsoo Kim; Garam Lee; Dokyoon Kim; Seung-Bo Lee; Seung Mi Lee (J. Biomed. Informatics, 2024)