2022

.

  IF Top 10%

  • U. Ju, L. L. Chuang, and C. Wallraven, “Acoustic Cues Increase Situational Awareness in Accident Situations: A VR Car-Driving Study,” IEEE Transactions on Intelligent Transportation Systems, Vol. 23, 2022, pp. 3281-3291.
  • C.-H. Han, G.-Y. Choi, and H.-J. Hwang, “Deep Convolutional Neural Network Based Eye States Classification Using Ear-EEG,” Expert Systems with Applications, Vol. 192, No. 15, 2022, p. 116443. 
  • W. Ko, E. Jeon, and H.-I. Suk, “A Novel RL-Assisted Deep Learning Framework for Task-Informative Signals Selection and Classification for Spontaneous BCIs,” IEEE Transactions on Industrial Informatics, Vol. 18, No. 3, 2022, pp. 1873-1882.
  • M. Lee, L. R. D. Sanz, A. Barra, A. Wolff, J. O. Nieminen, M. Boly, M. Rosanova, S. Casarotto, O. Bodart, J. Annen, A. Thibaut, R. Panda, V. Bonhomme, M. Massimini, G. Tononi, S. Laureys, O. Gosseries, and S.-W. Lee, “Quantifying Arousal and Awareness in Altered States of Consciousness using Interpretable Deep Learning,” Nature Communications, Vol. 13, No. 1064, 2022, pp. 1-14. 
  • D.-Y. Lee, J.-H. Jeong, B.-H. Lee, and S.-W. Lee, “Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 30, 2022, pp. 226-237. 
  • A.-K. Dombrowski, C. J. Anders, K.-R. Müller, and P. Kessel, “Towards Robust Explanations for Deep Neural Networks,” Pattern Recognition, Vol. 121, 2022, p. 108194. 

  IF Top 20%

  • S. Bak, J. Shin, and J. Jeong, “Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response,” Biosensors, Vol. 12, No. 1, 2022, pp. 1-18.

  Others

  • M. Shim, C.-H. Im, S.-H. Lee, and H.-J. Hwang, “Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder,” Frontiers in Neuroinformatics, Vol. 16, 2022, p. 811756.
  • W. Ko, E. Jeon, J. S. Yoon, and H.-I. Suk, “Semi-Supervised Generative and Discriminative Adversarial Learning for Motor Imagery-based Brain-Computer Interface,” Scientific Reports, Vol. 12, 2022, pp. 1-14.

2021

.

  IF Top 10%

  • J.-H. Cho, J.-H. Jeong, and S.-W. Lee, “NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using A Dual-Stage Deep Learning Framework,” IEEE Transactions on Cybernetics, 2021. (Accepted)
  • E. Jeon, W. Ko, J. S. Yoon, and H.-I. Suk, “Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI,” IEEE Transactions on Neural Networks and Learning Systems, 2021. (Accepted)
  • J.-S. Bang, M.-H. Lee, S. Fazli, C. Guan, and S.-W. Lee, “Spatio-Spectral Feature Representation for Motor Imagery Classification using Convolutional Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, 2021. (Accepted)
  • M. Leitheiser, D. Capper, P. Seegerer, A. Lehmann, U. Schüller, K.-R. Müller, F. Klauschen, P. Jurmeister, and M. Bockmayr, “Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation,” The Journal of Pathology, Vol. 256, No. 4, 2021, pp. 378-387.
  • G.-Y Choi, C.-H Han, H.-T Lee, N.-J Paik, W.-S Kim, and H.-J Hwang, “An Artificial Neural-Network Approach to Identify Motor Hotspot for Upper-Limb Based on Electroencephalography: A Proof-of-Concept Study,” Journal of NeuroEngineering and Rehabilitation, Vol. 18, No. 176, 2021, pp. 1-10.
  • F. Sattler, K.-R. Müller, and W. Samek, “Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 8, 2021, pp. 3710-3722.
  • D.-Y. Lee, M. Lee, and S.-W. Lee, “Decoding Imagined Speech Based on Deep Metric Learning for Intuitive BCI Communication,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, 2021, pp.1363-1374.
  • M. Lee, J.-H. Jeong, Y.-H. Kim, and S.-W. Lee, “Decoding Finger Tapping With the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29, 2021, pp. 1099-1109.
  • L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, K.-R. Müller, “A Unifying Review of Deep and Shallow Anomaly Detection,” Proceedings of the IEEE, Vol. 109, No. 5, 2021, pp. 756-795.
  • J.-Y. Kim, Y. J. Yun, J. Jeong, C.-Y. Kim, K.-R. Müller, and S.-W. Lee, “Leaf Inspired Homeostatic Cellulose Biosensors,” Science Advances, Vol. 7, No. 16, 2021, pp. 1-11.
  • W. Ko, E. Jeon, S. Jeong, and H.-I. Suk, “Multi-Scale Neural Network for EEG Representation Learning in BCI,” IEEE Computational Intelligence Magazine, Vol. 16, No. 2, 2021, pp. 31-45.
  • O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Shütt, A. Tkatchenko, K.-R. Müller, “Machine Learning Force Fields,” Chemical Reviews, Vol. 121, No. 16, 2021, pp. 10142-10186.
  • A. Binder, M. Bockmayr, M. Hägele, S. Wienert, D. Heim, K. Hellweg, M. Ishii, A. Stenzinger, A. Hocke, C. Denkert, K.-R. Müller, and F. Klauschen, “Morphological and Molecular Breast Cancer Profiling through Explainable Machine Learning,” Nature Machine Intelligence, Vol. 3, No. 4, 2021, pp. 355-366.
  • K. Zhang, N. Robinson, S.-W. Lee, and C. Guan, “Adaptive Transfer Learning for EEG Motor Imagery Classification with Deep Convolutional Neural Network,” Neural Networks, Vol. 136, 2021, pp. 1-10.
  • W. Samek , G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, “Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,” Proceedings of the IEEE, Vol. 109, No. 3, 2021, pp. 247-278.

  IF Top 20%

  • D. Kim, J. Jeong, and S. W. Lee, “Prefrontal solution to the bias-variance tradeoff during reinforcement learning,” Cell Reports, Vol. 37, No. 13, 2021, p. 110185.
  • Y.-E. Lee, G.-H. Shin, M. Lee, and S.-W. Lee, “Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running,” Scientific Data, Vol. 8, 2021, pp. 1-12.
  • S.-H. Kim, N. A. T. Nguyen, H.-J. Yang, and S.-W. Lee, “eRAD-Fe: Emotion Recognition-Assisted Deep Learning Framework,” IEEE Transactions on Instrumentation and Measurement, Vol. 70, 2021, pp. 1-12.
  • H. Seo and S. C. Jun, “Computational exploration of epidural cortical stimulation using a realistic head model,” Computers in Biology and Medicine, Vol. 135, 2021, p.104290.
  • T. Nierhaus, C. Vidaurre, C. Sannelli, K.-R, Müller, A. Villringer, “Immediate Brain Plasticity After One Hour of Brain-Computer Interface,” The Journal of Physiology, Vol. 599, No. 9, 2021, pp. 2435-2451.

  Others

  • M. Shim, G.-Y. Choi, N.-J. Paik, C. Lim, H.-J. Hwang, and W.-S. Kim, “Altered functional networks of alpha and low-beta bands during upper limb movement and association with motor impairment in chronic stroke,” Brain Connectivity, 2021. (Accepted)
  • Y. Pyo, J.-U. Woo, H.-G. Hwang, S. Nahm, and J. Jeong, “Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor,” Nanomaterials, Vol. 11, No. 10, 2021, pp. 1-15.
  • S. Dong, Y. Jin, S. Bak, B. Yoon, and J. Jeong, “Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity,” Electronics, Vol. 10, No. 23, 2021, pp. 1-20.
  • J. Seo and S. W. Lee, “Neural network-based intuitive physics for non-inertial reference frames,” IEEE Access, Vol. 9, 2021, pp. 114246-114254.
  • S. Studer, T. B. Bui, D. Drescher, A. Hanuschkin, L. Winkler, S. Peters, K.-R. Müller, “Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology,” Machine Learning and Knowledge Extraction, Vol. 3, No. 2, 2021, pp. 392-413.
  • K. Won, M. Kwon, M. Ahn, and S. C. Jun, “Selective subject pooling strategy to generalize models better in motor imagery BCI,” Sensors, Vol. 21, No. 16, 2021, pp. 1-18.
  • W. Ko, E. Jeon, S. Jeong, J. Phyo, and H.-I. Suk, “A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces,” Frontiers in Human Neuroscience, Vol. 15, 2021, p. 643386.
  • C. Vidaurre, T. Jorajuria, A. Ramos-Murguialday, K.-R. Müller, M. Gomez, V. V. Nikulin, “Improving Motor Imagery Classification During Induced Motor Perturbations,” Journal of Neural Engineering, Vol. 18, 2021, article 0460b1.
  • M. Shim, S.-H. Lee, H.-J. Hwang, “Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection,” Scientific reports, Vol. 11, 2021, pp. 1-7.
  • M. Shim, H.-J. Hwang, U. Kuhl, and H.-A. Jeon, “Resting-State Functional Connectivity in Mathematical Expertise,” Brain sciences, Vol. 11, No. 4, 2021, pp. 1-15.

2020

.

  IF Top 10%

  • Y.-E. Lee, N.-S. Kwak, and S.-W. Lee, “A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 12, 2020, pp. 2660-2670.
  • S.-H. Lee, M. Lee, and S.-W. Lee, “Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 12, 2020, pp. 2647-2659.
  • F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 9, 2020, pp. 3400-3413.
  • T. Kang, Y. Chen, S. Fazli, and C. Wallraven, “EEG-based Prediction of Successful Memory Formation during Vocabulary Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 11, 2020, pp. 2377-2389.
  • C.-H. Han, K.-R. Müller, and H.-J. Hwang, “Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 10, 2020, pp. 2102-2112.
  • O. A. von Lühmann, K.-R. Müller, and A. Tkatchenko, “Exploring chemical compound space with quantum-based machine learning,” Nature Reviews Chemistry, Vol. 4, 2020, pp.347-358.
  • O.-Y. Kwon, M.-H. Lee, C. Guan, and S.-W. Lee, “Subject-Independent Brain-Computer Interfaces based on Deep Convolutional Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 10, 2020, pp. 3839-3852.
  • N.-S. Kwak and S.-W. Lee, “Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces,” IEEE Transactions on Cybernetics, Vol. 50, No. 8, 2020, pp. 3654-3667.
  • J. Kauffmann, K.-R. Müller, and G. Montavon, “Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models,” Pattern Recognition, Vol. 101, 2020, p. 107198.
  • M. J. Idaji, K.-R. Müller, G. Nolte, B. Maess, A. Villringer, and V. V. Nikulin, “Nonlinear Interaction Decomposition (NID): A Method for Separation of Cross-Frequency Coupled Sources in Human Brain,” NeuroImage, Vol. 211, 2020, p. 116599.
  • O. A. von Lühmann, X. Li, K.-R. Müller, D. A. Boas, and M. A. Yücel, “Improved Physiological Noise Regression in fNIRS: A Multimodal Extension of the General Linear Model using Temporally Embedded Canonical Correlation Analysis,” NeuroImage, Vol. 208, 2020, p. 116472.
  • Noé, A. Tkatchenko, K.-R. Müller, and C. Clementi, “Machine Learning for Molecular Simulation,” Annual Review of Physical Chemistry, Vol. 71, 2020, pp. 361-390.
  • J.-H. Jeong, K.-H. Shim, D.-J. Kim, and S.-W. Lee, “Brain-Controlled Robotic Arm System based on Multi-Directional CNN-BiLSTM Network using EEG Signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 5, 2020, pp. 1226-1238.
  • J.-H. Jeong, N.-S. Kwak, C. Guan, and S.-W. Lee, “Decoding Movement-Related Cortical Potentials based on Subject-Dependent and Section-Wise Spectral Filtering,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 3, 2020, pp. 687-698.
  • K.-T. Kim, C. Guan, and S.-W. Lee, “A Subject-Transfer Framework based on Single-Trial EMG Analysis using Convolutional Neural Networks,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 1, 2020, pp. 94-103.
  • Y. Shi, H.-I. Suk, Y. Gao, S.-W. Lee, and D. Shen, “Leveraging Coupled Interaction for Multi-Modal Alzheimer’s Disease Diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 1, 2020, pp. 186-200.

  IF Top 20%

  • T. Kretz, K.-R. Müller, T. Schaeffter, and C. Elster, “Mammography Image Quality Assurance Using Deep Learning,” IEEE Transactions on Biomedical Engineering, Vol. 67, No. 12, 2020, pp. 3317-3326.
  • J.-H. Jeong, J.-H. Cho, K.-H. Shim, B.-H. Kwon, B.-H. Lee, D.-Y. Lee, D.-H. Lee, and S.-W. Lee, “Multimodal Signal Dataset for 11 Intuitive Movement Tasks from Single Upper Extremity during Multiple Recording Sessions,” GigaScience, Vol. 9, No. 10, 2020, p. giaa098.
  • D.-H. Lee, J.-H. Jeong, K. Kim, B.-W. Yu, and S.-W. Lee, “Continuous EEG Decoding of Pilots’ Mental States using Multiple Feature Block-based Convolutional Neural Network,” IEEE Access, Vol. 8, 2020, pp. 121929-121941.
  • S.-H. Kim, H.-J. Yang, N. A. T. Nguyen, R. M. Mehmood, and S.-W. Lee, “Parameter Estimation using Unscented Kalman Filter on the Gray-box Model for Dynamic EEG System Modeling,” IEEE Transactions on Instrumentation and Measurement, Vol. 14, No. 8, 2020, pp. 1-11.
  • J.-M. Lee, K.-H. Won, M.-Y. Kwon, S.-C. Jun, and M.-K. Ahn, “CNN With Large Data Achieves True Zero-Training in Online P300 Brain-Computer Interface,” IEEE Access, Vol. 8, 2020, pp. 74385-74400.
  • M. R. Song and S. W. Lee, “Dynamic Resource Allocation during Reinforcement Learning Accounts for Ramping and Phasic Dopamine Activity,” Neural Networks, Vol. 126, 2020, pp. 95-107.
  • Y. Park and W. Chung, “Optimal Channel Selection using Correlation Coefficient for CSP based EEG Classification,” IEEE Access, Vol. 8, 2020, pp. 111514-111521.
  • M. Lee, G.-H. Shin, and S.-W. Lee, “Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus,” IEEE Access, Vol. 8, 2020, pp. 107200-107213.
  • Y. Park and W. Chung, “A Novel EEG Correlation Coefficient Feature Extraction Approach Based on Demixing EEG Channel Pairs for Cognitive Task Classification,” IEEE Access, Vol. 8, 2020, pp. 87422-87433.
  • J.-H. Jeong, B.-H. Lee, D.-H. Lee, Y.-D. Yun, and S.-W. Lee, “EEG Classification of Forearm Movement Imagery using a Hierarchical Flow Convolutional Neural Network,” IEEE Access, Vol. 8, 2020, pp. 66941-66950.
  • Y. Kwak, K. Kong, W. J. Song, B. K. Min, and S. E. Kim, “Multilevel Feature Fusion with 3D Convolutional Neural Network for EEG Based Workload Estimation,” IEEE Access, Vol. 8, 2020, pp. 16009-16021.

  Others

  • E. Sauceda, M. Gastegger, S. Chmiela, K. -R. Müller, and A. Tkatchenko, “Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields,” The Journal of Chemical Physics, Vol. 153, No. 12, 2020, p. 124109.
  • M. Kwon, S. Han, K. Kim, and S. C. Jun, “Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study,” Sensors, Vol. 19, No. 5317, 2020, pp. 1-21.
  • S. H. Jin, S. H. Lee, S. T. Yang, and J. U. An, “Hemispheric Asymmetry in Hand Preference of Right-Handers for Passive Vibrotactile Perception: an fNIRS Study,” Scientific Reports, Vol. 10, 2020, pp. 1-10.
  • G. Taye, H.-J. Hwang, and K. M. Lim, “Application of a Convolutional Neural Network for Predicting the Occurrence of Ventricular Tachyarrhythmia using Heart Rate Variability Features,” Scientific Reports, Vol. 10, 2020, pp. 1-7.
  • M. Lee, J.-G. Yoon, and S.-W. Lee, “Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling,” Frontiers in Human Neuroscience, Vol. 14, 2020, pp. 1-15.
  • J. Choi, M. Kwon, and S. C. Jun, “A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies—Related Issues and Future Directions,” Sensors, Vol. 20, No. 10, 2020, p. 270.
  • M. Kwon, H. Cho, K. Won, M. Ahn, and S. C. Jun, “Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance,” Electronics, Vol. 9, No. 4, 2020, p. 690.
  • S. Xu, E. Adeli, J.-Z. Cheng, L. Xiang, Y. Li, S.-W. Lee, and D. Shen, “Mammographic Mass Segmentation using Multichannel and Multiscale Fully Convolutional Networks,” International Journal of Imaging Systems and Technology, Vol. 10, 2020, pp. 1-13.
  • J. Wang, S. Chmiela, K. R. Müller, F. Noé, and C. Clementi, “Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach,” Journal of Chemical Physics, Vol. 152, No. 19, 2020, p. 194106.
  • M. Hägele, P. Seegerer, S. Lapuschkin, M. Bockmayr, W. Samek, F. Klauschen, K.-R. Müller, and A. Binder, “Resolving Challenges in Deep Learning-based Analyses of Histopathological Images using Explanation Methods,” Scientific Reports, Vol. 10 No. 1, 2020, pp. 1-12.
  • C.-H. Han, K.-R. Müller, and H.-J. Hwang, “Brain-Switches for Asynchronous Brain-Computer Interfaces: A Systematic Review,” Electronics, Vol. 9, No. 3, 2020, p. 422.

2019

.

  IF Top 10%

  • S. Wiedemann, K-R. Müller, and W. Samek, “Compact and Computationally Efficient Representation of Deep Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 3, 2019, pp. 1-14.
  • K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, and R. J. Maurer, “Unifying Machine Learning and Quantum Chemistry with a Deep Neural Network for Molecular Wavefunctions,” Nature Communications, Vol. 10, No. 1, 2019, pp. 1-10.
  • M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, Sven Dähne, and P.-J. Kindermans, “iNNvestigate Neural Networks!,” Journal of Machine Learning Research, Vol. 20, No. 93, 2019, pp. 1-8.
  • D. Kim, G. Y. Park, J. P. O’Doherty, and S. W. Lee, “Task Complexity Interacts with State-Space Uncertainty in the Arbitration Between Model-based and Model-Free Learning,” Nature Communications, Vol. 10, No. 1, 2019. pp. 1-14.
  • P. Jurmeister, M. Bockmayr, P. Seegerer, T. Bockmayr, D. Treue, G. Montavon, C. Vollbrecht, A. Arnold, D. Teichmann, K. Bressem, U. Schüller, M. von Laffert, K.-R. Müller, D. Capper, F. Klauschen, “Machine Learning Analysis of DNA Methylation Profiles Distinguishes Primary Lung Squamous Cell Carcinomas from Head and Neck Metastases,” Science Translational Medicine, Vol. 11, No. 509, 2019, pp. 1-10.
  • J. H. Lee, B. Seymour, J. Z. Leibo, S. J. An, and S. W. Lee, “Towards High Performance, Memory Efficient, and Fast Reinforcement Learning – Lessons from Decision Neuroscience,” Science Robotics, Vol. 4, No. 26, 2019, pp. 2975.
  • C. Vidaurre, G. Nolte, I.-E.-J. de Vries, M. Gómez, T.-W. Boonstra, K.-R. Müller, A. Villringer, and V.-V. Nikulin, “Canonical Maximization of Coherence: A Novel Tool for Investigation of Neuronal Interactions between Two Datasets,” NeuroImage, Vol. 201, 2019, pp. 116009.
  • O. A. von Lühmann, Z. Boukouvalas, K.-R. Müller, and T. Adalı. “A New Blind Source Separation Framework for Signal Analysis and Artifact Rejection in Functional Near-Infrared Spectroscopy,” NeuroImage, Vol. 201, 2019, pp. 72-88.
  • C. Vidaurre, A.-R. Murguialday, S. Haufe, M. Gómez, K.-R. Müller, and V.-V. Nikulin, “Enhancing Sensorimotor BCI Performance with Assistive Afferent Activity: An Online Evaluation,” NeuroImage, Vol. 199, 2019, pp. 375-386.
  • Y.-K. Park and W.-Z. Chung, “Frequency-optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 27, No. 7, 2019, pp. 1378-1388.
  • S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K.-R. Müller, “Unmasking Clever Hans Predictors and Assessing What Machines Really Learn,” Nature Communications, Vol. 10, No. 1, 2019, article 1096.
  • S. Chmiela, H. E. Sauceda, I. Poltavsky, K.-R. Müller, and A. Tkatchenko, “sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning,” Computer Physics Communications, Vol. 240, 2019, pp. 38-45.
  • L. Fang, L. Zhang, D. Nie, X. Cao, I. Rekik, S.-W. Lee, H. He, and D. Shen, “Automatic Brain Labeling via Multi-Atlas Guided Fully Convolutional Networks,” Medical Image Analysis, Vol. 51, No. 2019, pp. 157-168.
  • J. Jeong and S. Dong, “Onset Classification in Hemodynamic Signals Measured during Three Working Memory Tasks Using Wireless Functional Near-Infrared Spectroscopy,” IEEE Journal of Selected Topics in Quantum Electronics, Vol. 25, No. 1, 2019, article 7102211.
  • H. Huang, X. Liu, Y. Jin, S.-W. Lee, C.-Y. Wee, and D. Shen, “Enhancing the Representation of Functional Connectivity Networks by Fusing Multi‐view Information for Autism Spectrum Disorder Diagnosis,” Human Brain Mapping, Vol. 40, No. 3, 2019, pp. 833-854.

  IF Top 20%

  • M.-H. Lee, O.-Y. Kwon, Y.-J. Kim, H.-K. Kim, Y.-E. Lee, J. Williamson, S. Fazli, and S.-W. Lee, “EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, Vol. 8, No. 5, 2019, pp. 1-16.
  • S. H. Lee, S. H. Jin, and J. An, “The Difference in Cortical Activation Pattern for Complex Motor Skills: A Functional Near-infrared Spectroscopy Study,” Scientific Reports, Vol. 9, 2019, article 5175.
  • G.-Y. Choi, C.-H. Han, Y.-J. Jung, and H.-J. Hwang, “A Multi-day and Multi-band Dataset for Steady-state Visual Evoked Potential-based Brain-Computer Interface,” Gigascience, Vol. 8, No. 11, 2019, pp. 1-11.
  • S. W. Lee and B. Seymour, “Decision-making in Brains and Robots-The Case for an Interdisciplinary Approach,” Current Opinion in Behavioral Sciences, Vol. 26, 2019, pp. 137-145.
  • Y.-K. Park and W.-Z. Chung, “Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems,” Sensors, Vol. 19, No. 17, 2019, article 3769.
  • J. Y. Choi, K. H. Won, and S. C. Jun, “Acoustic Stimulation Following Sleep Spindle Activity May Enhance Procedural Memory Consolidation During a Nap,” IEEE Access, Vol. 7, 2019, pp. 56297-56307.
  • S. Dong and J. Jeong, “Improvement in Recovery of Hemodynamic Responses by Extended Kalman Filter with Non-Linear State-Space Model and Short Separation Measurement,” IEEE Transactions on Biomedical Engineering, Vol. 66, No. 8, 2019, pp. 2152-2162.
  • K. T. Schütt, P. Kessel, M. Gastegger, K.-A. Nicoli, A. Tkatchenko, and K.-R. Müller, “SchNetPack: A Deep Learning Toolbox for Atomistic Systems,” Journal of Chemical Theory and Computation, Vol. 15, No. 1, 2019, pp. 448-455.
  • Y. Zhang, H. Zhang, X. Chen, M. Liu, X. Zhu, S.-W. Lee, and D. Shen, “Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis,” Pattern Recognition,“ Vol. 88, 2019, pp. 421-430.
  • R. Yu, L. Qiao, M. Chen, S.-W. Lee, X. Fei, and D. Shen, “Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification,” Pattern Recognition, Vol. 90, 2019, pp. 220-231.
  • F. Horst, S. Lapuschkin, W. Samek, K.-R. Müller, and W. I. Schöllhorn, “Explaining the Unique Nature of Individual Gait Patterns with Deep Learning,” Scientific Reports, Vol. 9, No. 1, 2019, article 2391.
  • M. Lee, B. Baird, O. Gosseries, J. O. Nieminen, M. Boly, B. Postle, G. Tononi, and S.-W. Lee, “Connectivity Differences between Consciousness and Unconsciousness in Non-rapid Eye Movement Sleep: a TMS–EEG Study,” Scientific Reports, Vol. 9, 2019, article 5175.
  • C. K. Im, H. Seo, and S. C. Jun, “Geometrical Variation’s Influence on the Effects of Stimulation may be Important in the Conventional and Multi-array tDCS – Comparison of Electrical Fields Computed,” IEEE Access, Vol. 7, 2019, pp. 8557-8569.
  • S. Kaltenstadle, S. Nakajima, K.-R. Muller, and W. Samek, “Wasserstein Stationary Subspace Analysis,” IEEE Journal of Selected Topics in Signal Processing,” Vol. 12, No. 6, 2019, pp.1213-1223.

  Others

  • S. Bak, J. Park, J. Shin, and J. Jeong, “Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping,” Electronics, Vol. 8, No. 12, 2019, p. 1486.
  • S. Lee, H. Cho, K. Kim, and S. C. Jun, “Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues,” Sensors, Vol. 19, No. 20, 2019, pp. 4592-4611.
  • Y. Hou, X. Qu, G. Liu, S.-W. Lee, and D. Shen, “Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement,” Mathematical Problems in Engineering, Vol. 2019, No. 6395147, 2019, pp. 1-14.
  • M. Lee, C.-B. Song, G.-H. Shin, and S.-W. Lee, “Possible Effect of Binaural Beat Combined with Autonomous Sensory Meridian Response for Inducing Sleep,” Frontiers in Human Neuroscience, Vol. 13, No. 425, 2019, pp. 1-16.
  • S. Hwang, K. Hong, G. Son, and H. Byun, “Learning CNN Features from DE Features for EEG-based Emotion Recognition,” Pattern Analysis and Applications, Vol. 23, No. 1, 2019, pp. 1-13.
  • S.-I. Choi and H.-J Hwang, “Effects of Different Re-referencing Methods on Ear-EEG”, Frontiers in Neuroscience, Vol. 13, No. 822, 2019, pp. 1-13.
  • K. H. Won, M. Y. Kwon, S. H. Jang, M. K. Ahn, and S. C. Jun, “P300 Speller Performance Predictor Based on RSVP Multi-feature,” Frontiers in Human Neuroscience, Vol. 13, 2019, article 261.
  • X. Zhu, H.-I. Suk, and D. Shen, “Low-Rank Dimensionality Reduction for Multi-Modality Neurodegenerative Disease Identification,” World Wide Web, Vol. 22, No. 2, 2019, pp. 907-925.
  • X. Zhu, H.-I. Suk, and D. Shen, “Group Sparse Reduced Rank Regression for Genetic Study,” World Wide Web, Vol. 22, No. 2, 2019, pp. 673-688.
  • X. Zhu, H.-I. Suk, S.-W. Lee, and D. Shen. “Discriminative Self-Representation Sparse Regression for Neuroimaging-based Alzheimer’s Disease Diagnosis.” Brain Imaging and Behavior, Vol. 13, No. 1, 2019, pp. 27-40. 
  • D.-O. Won, B.-R. Lee, K.-S. Seo, H.-J. Kim, and S.-W. Lee, “Alteration of Coupling between Brain and Heart induced by Sedation with Propofol and Midazolam,” PLOS One, Vol. 14, No. 7, 2019, pp. e0219238-e0219257.
  • L. Helmers, F. Horn, F. Biegler, T. Oppermann, and K.-R. Müller, “Automating the search for a patent’s prior art with a full text similarity search,” PLOS One, Vol. 14, No. 3, 2019, pp. e0212103-e0212119.
  • H. E. Sauceda, S. Chmiela, I. Poltavsky, K.-R. Müller, and A. Tkatchenko, “Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces,” The Journal of Chemical Physics, Vol. 150, No. 11, 2019, pp. 1-11.
  • G. Schwenk, R. Pabst, and K.-R. Müller, “Classification of Structured Validation Data Using Stateless and Stateful Features,” Computer Communications, Vol. 138, 2019, pp. 54-66. 
  • C. Sannelli, C. Vidaurre, K.-R. Müller, and B. Blankertz, “A Large Scale Screening Study with a SMR-based BCI: Categorization of BCI Users and Differences in their SMR Activity,” PLOS One, Vol. 14, No. 1, 2019, pp. e0207351-e0207387.

2018

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  IF Top 10%

  • D.-O. Won, H.-J. Hwang, D.-M. Kim, K.-R. Muller, and S.-W. Lee, “Motion-based Rapid Serial Visual Presentation for Gaze-Independent Brain-Computer Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, No. 2, 2018, pp. 334-343.
  • M.-H. Lee, J. Williamson, D.-O. Won, S. Fazli, and S.-W. Lee, “A High Performance Spelling System based on EEG-EOG Signals with Visual Feedback,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, No. 7, 2018, pp. 1443-1459.
  • H. Seo and S. C. Jun, “Relation between the Electric Field and Activation of Cortical Neurons in Transcranial Electrical Stimulation,” Brain Stimulation, Vol. 551, No. 2, pp. 661-671.
  • D. Hubner, T. Verhoeven, K.-R. Muller, P-J. Kindermans, and M. Tangermann, “Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison,” IEEE Computational Intelligence Magazine, Vol. 13, No. 2, 2018, pp. 66-77.
  • K.-H. Thung, P.-T. Yap, E. Adeli, S.-W. Lee, and D. Shen, “Conversion and Time-to-Conversion Predictions of Mild Cognitive Impairment using Low-Rank Affinity Pursuit Denoising and Matrix Completion,” Medical Image Analysis, Vol. 45, 2018, pp. 68-82. 
  • S. Bosse, D. Maniry, K.-R. Muller, T. Wiegand, and W. Samek, “Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment,” IEEE Transactions on Image Processing, Vol. 27, No. 1, 2018, pp. 206-219.
  • S. Ahn, H. Cho, M. Kwon, K. Kim, H. Kwon, B. S. Kim, W. S. Chang, J. W. Chang, and S. C. Jun, “Interbrain Phase Synchronization during Turn-taking Verbal Interaction – A Hyperscanning Study using Simultaneous EEG/MEG,” Human Brain Mapping, Vol. 39, No. 1, 2018, pp. 171-188.
  • E. Jun, E. Kang, J. Choi, and H.-I. Suk, “Modeling Regional Dynamics in Low-Frequency Fluctuation and Its Application to Autism Spectrum Disorder Diagnosis,” NeuroImage, Vol. 184, No. 1, 2018, pp. 669-686.

  IF Top 20%

  • J. Shin, O. A. von Lühmann, D.-W. Kim, J. Mehnert, H.-J. Hwang, and K.-R. Muller, “Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset,” Scientific Data, Vol. 5, 2018, article 180003.
  • S. Dong and J. Jeong, “Process-specific Analysis in Episodic Memory Retrieval using Fast Optical Signals and Hemodynamic Signals in the Right Prefrontal Cortex,” Journal of Neural Engineering, Vol. 15, No. 1, 2018, article 015001.
  • S. H. Lee, S. H. Jin, and J. An, “Distinction of Directional Coupling in Sensorimotor Networks between Active and Passive Finger Movements using fNIRS,” Biomedical Optics Express, Vol. 9, No. 6, 2018, pp. 2859-2870.
  • F. Alimardani, J.-H. Cho, R. Boostanim, and H.-J, Hwang, “Classification of Bipolar Disorder and Schizophrenia using Steady-State Visual Evoked Potential based Features,” IEEE Access, Vol. 6, 2018, pp. 40379-40388.
  • W. Pronobis, A. Tkatchenko, and K.-R. Muller, “Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules,” Journal of Chemical Theory and Computation, Vol. 14, No. 6, 2018, pp. 2991-3003. 
  • H. Huang, J. Lu, J. Wu, Z. Ding, S. Chen, L. Duan, J.-L. Cui, F. Chen, D.-Z. Kang, L. Qi, W. Qiu, S.-W. Lee, S. Qiu, D. Shen, Y.-F. Zang, and H. Zhang, “Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis,” Scientific Reports, Vol. 8, No. 1, 2018, article 1223. 
  • J. Shin, D.-W. Kim, K.-R. Muller, and H.-J, Hwang, “Improvement of Information Transfer Rate by Hybrid EEG-NIRS Brain-Computer Internet with Short Task Duration: Offline and Pseudo-Online Analysis,” Sensors, Vol. 18, No. 6, 2018, pp. 1-16.
  • S.-I. Choi, C.-H. Han, G.-Y. Choi, J. Shin, K. S. Song, C.-H. Im, and H.-J, Hwang, “On the Feasibility of Using Ear-EEG to Develop an Endogenous Brain-Computer Interface,” Sensors, Vol. 18, No. 9, 2018, pp. 1-14. 
  • G. Lee, S. Jin, and J. An, “Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network,” Sensors, Vol. 18, No. 9, 2018, article 2957.

  Others

  • J. Shin, K.-R. Muller, and H.-J, Hwang, “Eyes-Closed Hybrid Brain-Computer Interface Employing Frontal Brain,” PLOS One, Vol. 13, No. 5, 2018, article e0196359.
  • M. Ahn, H. Cho, S. Ahn, and S. C. Jun, “User’s Self-Prediction of Performance in Motor Imagery Brain-Computer Interface,” Frontiers in Human Neuroscience, Vol. 12, 2018, article 59.
  • W. Pronobis, K. T. Schutt, A. Tkatchenko, and K.-R. Muller, “Capturing Intensive and Extensive DFT/TDDFT Molecular Properties with Machine Learning,” European Physical Journal B, Vol. 91, No. 8, 2018, article 178.
  • K. T. Schutt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Muller, “SchNet – A Deep Learning Architecture for Molecules and Materials,” Journal of Chemical Physics, Vol. 148, No. 24, 2018, article 241722.
  • G. Montavon, W. Samek, and K.-R. Muller, “Methods for Interpreting and Understanding Deep Neural Networks,” Digital Signal Processing, Vol. 73, 2018, pp. 1-15.

2017

.

  IF Top 10%

  • O.-H. Choung, S. W. Lee, and Y. Jeong, “Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task,” Scientific Reports, Vol. 7, 2017, 17676.
  • M. Lee, R. D. Sanders, S.-K. Yeom, D.-O. Won, K.-S. Seo, H. J. Kim, G. Tononi, and S.-W. Lee, “Network Properties in Transitions of Consciousness during Propofol-Induced Sedation,” Scientific Reports, Vol. 7, 2017, article 16791.
  • F. Brockjerde, L. Vogt, L. Li, M.E. Tuckerman, K. Burke, and K.-R. Muller, “Bypassing the Kohn-Sham Equations with Machine Learning,” Nature Communications, Vol. 8, No. 1, 2017, article 872.
  • W. Liu, I.W. Tsang, and K.-R. Muller, “An Easy-to-Hard Learning Paradigm for Multiple Classes and Multiple Labels,” Journal of Machine Learning Research, Vol. 18, 2017, pp. 1-38.
  • 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, 2017, pp. 5804-5821.
  • Y. Zhang, H. Zhang, X. Chen, S.-W. Lee, and D. Shen, “Hybrid High-order Functional Connectivity Networks using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis,” Scientific Reports, Vol. 7, 2017, article 6530.
  • X. Chen, H. Zhang, L. Zhang, C. Shen, S.-W. Lee, and D. Shen, “Extraction of Dynamic Functional Connectivity from Brain Grey Matter and White Matter for MCI Classification,” Human Brain Mapping, Vol. 38, No. 10, 2017, pp. 5019-5034.
  • H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, “EEG Datasets for Motor Imagery Brain-Computer Interface,” GigaScience, Vol. 6, No. 1, 2017, pp. 1-8.

  IF Top 20%

  • W. Samek, S. Nakajima, M. Kawanabe, and K.-R. Muller, “On Robust Parameter Estimation in Brain-Computer Interfacing,” Journal of Neural Engineering, Vol. 14, No. 6, 2017, pp. 1-18.
  • S. Ahn and S. C. Jun, “Multi-modal Integration of EEG-fNIRS for Brain-computer Interfaces – Current Limitations and Future Directions,” Frontiers in Human Neuroscience, Vol. 11, 2017, article 503.

  Others

  • J. An, G. Lee, S. H. Lee, and S. H. Jin, “Selective Detrending using Baseline Drift Detection Index for Task-dependant fNIRS Signal,” Advances in Science, Technology and Engineering Systems Journal, Vol. 2, No. 3, 2017, pp. 1147-1151.
  • S.-K. Yeom, D.-O. Won, S. I. Chi, K.-S. Seo, H. J. Kim, K.-R. Muller, and S.-W. Lee, “Spatio-temporal Dynamics of Multimodal EEG-fNIRS Signals in the Loss and Recovery of Consciousness under Sedation using Midazolam and Propofol,” PLOS One, Vol. 12, No. 11, 2017, article e0187743.
  • H.-J. Hwang, J. M. Hahne, and K.-R. Muller, “Real-time Robustness Evaluation of Regression based Myoelectric Control against Arm Position Change and Donning/doffing,” PLOS One, Vol. 12, No. 11, 2017, article e0186318.
  • L. Arras, F. Horn, G. Montavon, K.-R. Muller, and W. Samek, ““What is Relevant in a Text Document?”: An Interpretable Machine Learning Approach,” PLOS One, Vol. 12, No. 8, 2017, article e0181142.