2020

.

  IF Top 10%

  • 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, 2020. (Accepted)
  • 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, 2020. (Accepted)
  • 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, 2020. (Accepted)
  • 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%

  • 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, 2020. (Accepted)
  • T. Kretz, K.-R. Müller, T. Schaeffter, and C. Elster, “Mammography Image Quality Assurance Using Deep Learning,” IEEE Transactions on Biomedical Engineering, 2020. (Accepted)
  • 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.
  • 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

  • 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,” 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.
  • 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. 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.
  • 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.
  • 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%

  • 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

.

  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.