● Jun-Mo Kim, Keun-Soo Heo, Dong-Hee Shin, Hyeonyeong Nam, Dong-Ok Won, Ji-Hoon Jeong, and Tae-Eui Kam, "A Learnable Continuous Wavelet-based Multi-Branch Attentive Convolutional Neural Network for Spatio-Spectral-Temporal EEG Signal Decoding," Expert Systems with Applications, 2024. (Accepted)
● Young-Han Son*, Dong-Hee Shin*, and Tae-Eui Kam, “FTMMR: Fusion Transformer for Integrating Multiple Molecular Representations”, IEEE Journal of Biomedical and Health Informatics, 2024. (Accepted)
● J.-H. Jeong, I.-G. Lee, S.-K. Kim, Tae-Eui Kam, S.-W. Lee, and E. Lee, "DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework," IEEE Journal of Biomedical and Health Informatics, 2024. (Accepted)
● Deok-Joong Lee, Dong-Hee Shin, Young-Han Son, Ji-Wung Han, Ji-Hye Oh, Da-Hyun Kim, Ji-Hoon Jeong and Tae-Eui Kam, “Spectral Graph Neural Network-based Multi-atlas Brain Network Fusion for Major Depressive Disorder Diagnosis,” IEEE Journal of Biomedical and Health Informatics, 2024. (Accepted)
● Minjoo Lim, Keun-Soo Heo, Jun-mo Kim, Bogyeong Kang, Weili Lin, Han Zhang, Dinggang Shen, and Tae-Eui Kam, "A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising" IEEE Journal of Biomedical and Health Informatics, 2024. (Accepted)
● Dong-Hee Shin*, Young-Han Son*, Jun-Mo Kim, Hee-Jun Ahn, Jun-Ho Seo, Chang-Hoon Ji, Ji-Wung Han, Byung-Jun Lee, Dong-Ok Won, and Tae-Eui Kam, "MARS: Multi-Agent Reinforcement Learning for Spatial-Spectral and Temporal Feature Selection in EEG-based BCI" IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, 2024. (Accepted)
● Ji-Hye Oh, Deok-Joong Lee, Chang-Hoon Ji, Dong-Hee Shin, Ji-Wung Han, Young-Han Son, and Tae-Eui Kam, “Graph-based Conditional Generative Adversarial Networks for Major Depressive Disorder Diagnosis with Synthetic functional Brain Network Generation,” IEEE Journal of Biomedical and Health Informatics, Vol. 28, 2024.
● Ji-Wung Han*, Soyeon Bak*, Jun-Mo Kim, WooHyeok Choi, Dong-Hee Shin, Young-Han Son, and Tae-Eui Kam, "META-EEG: Meta-learning-based class-relevant EEG representation learning for zero-calibration brain–computer interfaces," Expert Systems with Applications, Vol. 238, Part D, 121986, 2024.
● D.-H. Lee, J.-H. Jeong, B.-W. Yu, Tae-Eui Kam, and S.-W. Lee, "Autonomous System for EEG-based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment," IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 53, No. 10, October, 2023.
● Hyeonyeong Nam, Jun-Mo Kim, WooHyeok Choi, Soyeon Bak, and Tae-Eui Kam, "The Effects of Layer-wise Relevance Propagation-based Feature Selection for EEG Classification: A Comparative Study on Multiple Datasets," Frontiers in human neuroscience, Vol. 17, 2023.
● Da-Hyun Kim, Dong-Hee Shin, and Tae-Eui Kam, "Bridging the BCI Illiteracy Gap: A Subject-to-Subject Semantic Style Transfer for EEG-based Motor Imagery Classification," Frontiers in human neuroscience, Vol. 17, 2023.
● J.-S. Bang, D.-O. Won, Tae-Eui Kam, and S.-W. Lee, "Motion Sickness Prediction based on Dry EEG in Real Driving Environment," IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 5, pp. 5442-5455, 2023.
● Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam, “Deep Attentive Spatio-Temporal Feature Learning for Automatic Resting-State fMRI Denoising,” NeuroImage, Vol. 254, No. 1, Article 119127, 2022.
● T.-E. Kam, H. Zhang, Z. Jiao, and D. Shen, “Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection,” IEEE Transactions on Medical Imaging, Vol. 39, No. 2, pp. 478-487, 2020.
● T.-E. Kam, H.-I. Suk, and S.-W. Lee, “Multiple Functional Networks Modeling for Autism Spectrum Disorder Diagnosis,” Human Brain Mapping, Vol. 38, No. 11, pp. 5804-5821, 2017.
● T.-E. Kam, D. J. Mannion, S.-W. Lee, K. Doerschner, and D. J. Kersten, “Human Visual Cortical Responses to Specular and Matte Motion Flows,” Frontiers in Human Neuroscience, Vol. 9, Article 579, 2015.
● T.-E. Kam, H.-I. Suk, and S.-W. Lee, “Non-Homogeneous Spatial Filter Optimization for ElectroEncephaloGram (EEG)-Based Motor Imagery Classification,” Neurocomputing, Vol. 108, No. 2, pp. 58-68, 2013.
● Dong-Hee Shin*, Young-Han Son*, Deok-Joong Lee, Ji-Wung Han and Tae-Eui Kam, "DyMol: Dynamic Many-Objective Molecular Optimization with Objective Decomposition and Progressive Optimization", International Conference on Learning Representations (ICLR) Workshop GEM, Vienna Austria, May. 7-11, 2024.
● Jun-Mo Kim, Soyeon Bak, Hyeonyeong Nam, WooHyeok Choi, Da-Hyun Kim, Tae-Eui Kam, "Meta-Learning-based Cross-Dataset Motor Imagery Brain-Computer Interface”, Proc. 12th IEEE International Winter Conference on Brain-Computer Interface, Korea, Feb. 26-28, 2024.
● Sanghyeon Cho, Bogyeong Kang, Keun-soo Heo, Eunjung Jo, Tae-Eui Kam,"Enhanced Structure Preservation and Multi-View Approach in Unsupervised Domain Adaptation for Optic Disc and Cup Segmentation", IEEE 21st International Symposium on Biomedical Imaging (ISBI), Athens Greece, May. 27-30, 2024.
● Jun-Mo Kim, Soyeon Bak, Hyeonyeong Nam, WooHyeok Choi, Da-Hyun Kim, Tae-Eui Kam, "SAT-Net: SincNet-Based Attentive Temporal Convolutional Network for Motor Imagery Classification", 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), USA, Oct.1-4, 2023.
● Hyeonyeong Nam, Jun-Mo Kim, and Tae-Eui Kam, "Feature Selection Based on Layer-Wise Relevance Propagation for EEG-based MI classification”, Proc. 11th IEEE International Winter Conference on Brain-Computer Interface, Korea, Feb. 20-22, 2023.
● Young-Han Son*, Dong-Hee Shin*, Ji-Wung Han, Seong-Hyeon Won, Tae-Eui Kam, "GNN-based Antibody Structure Prediction using Quaternion and Euler Angle Combined Representation”, Proc. 7th International Conference on Consumer Electronics (ICCE) Asia, Korea, Oct. 26-28, 2022.
● Bogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, Tae-Eui Kam, "Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation", Proc, MICCAI BrainLes 2022 workshop, Singapore, Sep. 18-22, 2022.
● Dong-Hee Shin, Dong-Hee Ko, Ji-Wung Han, Tae-Eui Kam, "Evolutionary Reinforcement Learning for Automated Hyperparameter Optimization in EEG Classification”, Proc. 10th IEEE International Winter Conference on Brain-Computer Interface, Korea, Feb. 21-23, 2022.
● Dong-Hee Ko, Dong-Hee Shin, Tae-Eui Kam, "Attention-based spatio-temporal-spectral feature learning for subject-specific EEG classification”, Proc. 9th IEEE International Winter Conference on Brain-Computer Interface, Korea, Feb. 22-24, 2021.
● Mayssa Soussia, Xuyun Wen, Zhen Zhou, Bing Jin, Tae-Eui Kam, Li-Ming Hsu, Zhengwang Wu, Gang Li, Li Wang, Islem Rekik, Weili Lin, Dinggang Shen, Han Zhang, "A Computational Framework for Dissociating Development-Related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy", Proc. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Lima, Peru, Oct 4-8, 2020.
● Eunjin Jeon, Eunsong Kang, Jiyeon Lee, Jaein Lee, Tae-Eui Kam, and Heung-Il Suk, Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis, Proc. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Lima, Peru, Oct 4-8, 2020.
● T.-E. Kam, X. Wen, B. Jin, Z. Jiao, L.-M. Hsu, Z. Zhou, Y. Liu, K. Yamashita, S.-C. Hung, W. Lin, H. Zhang, and D. Shen, and the UNC/UMN Baby Connectome Project Consortium, “A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI”, Proc. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, Oct 13-17, 2019.
● Z. Jiao, T.-E. Kam, Y. Wu, E. M. Hossein, P. Huang, L.-M. Hsu, H. Zhang, and D. Shen, “Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis”, Proc. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, Oct 13-17, 2019.
● T.-E. Kam, H. Zhang, B. Jing, X. Wen, W. Lin, D. Shen, for UNC/UMN Baby Connectome Project Consortium, “Deep Learning-based Automatic Noisy Component Detection for Automatic Resting-state fMRI Denoising,” Proc. The Organization for Human Brain Mapping (OHBM), Rome, Italy, June 9-13, 2019.
● L.-M. Hsu, H. Zhang, X. Wen, B. Jing, T.-E. Kam, L. Wang, Z. Wu, P.-T. Yap, K. R. Baluyot, B. R. Howell, M. A. Styner, E. Yacoub, G. Chen, T. Potts, J. H. Gilmore, J. Piven, J. K. Smith, K. Ugurbil, H. Zhu, H. Hazlett, J. T. Elison, W. Lin, D. Shen, for UNC/UMN Baby Connectome Project Consortium, “Frequency specificity of spontaneous brain activity in developing infant brain,” Proc. The Organization for Human Brain Mapping (OHBM), Rome, Italy, June 9-13, 2019.
● H. Zhang, X. Wen, B. Jing, L.-M. Hsu, T.-E. Kam, Z. Wu, L. Wang, G. Li, W. Lin, D. Shen, for UNC/UMN Baby Connectome Project Consortium, “Infant Resting-state FMRI Analysis Pipeline for UNC/UMN Baby Connectome Project,” Proc. The Organization for Human Brain Mapping (OHBM), Rome, Italy, June 9-13, 2019.
● H. Zhang, G. Li, X. Wen, B. Jing, L.-M. Hsu, T.-E. Kam, W. Lin, D. Shen, for UNC/UMN Baby Connectome Project Consortium, “Month-to-month Development of Brain Functional Networks during Early Infancy,” Proc. The Organization for Human Brain Mapping (OHBM), Rome, Italy, June 9-13, 2019.
● T.-E. Kam, H. Zhang, and D. Shen, “A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis”, Proc. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain, Sep. 16-20, 2018, pp. 293-301.
● T.-E. Kam, D. Mannion, S.-W. Lee, K. Doerschner, and D. Kersten, “An fMRI Study of Cortical Responses for Reflectance-specific Image Motion,” Proc. The Annual Meeting of the Vision Science Society (VSS), Naples, Florida May 10 – 15, 2013.
● T.-E. Kam, H.-I. Suk, and S.-W. Lee, “Non-homogeneous spatial filter optimization for EEG-based brain-computer interfaces,” IEEE International Winter Workshop on Brain-Computer Interface (BCI), Gangwon Province, South Korea, February 18-20, 2013.
● T.-E. Kam, D. Kersten, R. Fleming, S.-W. Lee, and K. Doerschner, “Visual adaptation to reflectance-specific image motion,” Proc. The Annual Meeting of the Vision Science Society (VSS), Naples, Florida May 11 – 16, 2012.
● D. Kersten, T.-E. Kam, D. Mannion, and S.-W. Lee, “Neural Mechanisms of Material Perception,” Proc. 12th China-Japan-Korea Joint Workshop on Neurobiology and Neuroinformatics, Seoul, Korea, Nov. 21-23, 2012, OS-06.
● T.-E. Kam and S.-W. Lee, “Time-dependent common spatial patterns optimization for EEG signal classification,” The First Asian Conference on Pattern Recognition, Beijing, China, Nov 28-30, 2011.
● T.-E. Kam and S.-W. Lee, “Optimizing time-dependent discriminative spatial filter for EEG-based multi-class motor imagery classification,” International Workshop on Pattern Recognition in NeuroImaging (PRNI), Seoul, South Korea, May 16-18, 2011.
● 감태의, 석흥일, 이성환, “자폐증 진단을 위한 휴지-상태 기능성자기공명영상의 군집화 기반 멀티-네트워크 모델링”, 2015년 한국뇌공학회 뇌인지공학 심포지엄 발표논문집, 고려대학교, 2015년 7월, pp. 1-2
● 감태의, 이성환, “표면 재질 인지를 위한 시지각 처리 과정의 수리적 분석”, 2014년 한국컴퓨터종합학술대회 논문집, 부경대학교, 2014년 6월, pp. 811-812.
● 감태의, 이성환, “시지각 인지 과정 이해를 위한 행동 및 수리적 모델 비교”, 2014 한국인지과학학회 학술대회, 서울대학교, 2014년 5월.
● 감태의, D. Mannion, D. Kersten, 이성환, “Visual Perception of Surface Materials”, 2013 뇌와 인공지능 심포지엄, 정선, Feb. 20-21, 2013, PS-15.
● 감태의, D. Kersten, 이성환, “움직이는 물체의 표면 재질에 대한 시각 적응”, 2012 한국인지과학회 춘계학술대회논문집, 고려대학교, 2012년 6월, pp. 105.
● 감태의, D. Kersten, 이성환, “Reflectance Adaptation to Surface Material of Moving Objects”, 뇌와 인공지능 심포지엄, 2012년 1월 31일-2월 1일, PS-40.
● 감태의, 이성환, “비 동질 공간 필터 최적화 기반의 동작 상상 EEG 신호 분류”, 2011 한국컴퓨터종합학술대회 논문집, 제1(A)호, 경주교육문화회관, 2011년 6월, pp. 469-472.
● 감태의, 이성환, “EEG 기반의 멀티 클래스 동작 상상 분류를 위한 시간-주파수 공간에서의 적응적 근원 신호 선택”, 제37회 한국정보과학회 추계학술발표회 발표 논문집, 제2(C)호, 단국대학교, 2010년 11월, pp. 199-202.
● 이성환, 감태의, 석흥일, “다중 뇌 연결망 구축을 통한 뇌 상태 모니터링 방법 및 장치”, 등록번호: 10-1796055, 등록일: 2017.11.03.