Article
Automation & Control Systems
Chi Man Wong, Ze Wang, Agostinho C. Rosa, C. L. Philip Chen, Tzyy-Ping Jung, Yong Hu, Feng Wan
Summary: This study investigates the feasibility of transferring model parameters in SSVEP-based BCIs across different groups of visual stimuli. Experiment results show that spatial filters share commonality across different frequencies and impulse responses share commonality across neighboring frequencies. The tlCCA algorithm performs significantly better than calibration-free algorithms and comparably to calibration-based algorithms in recognition performance.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Neurosciences
Xiaobing Liu, Bingchuan Liu, Guoya Dong, Xiaorong Gao, Yijun Wang
Summary: The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) offers high-speed alternative and augmentative communication in real-world applications. This study demonstrates that within-subject transfer learning can improve BCI performance and reduce calibration burden. The results show that the transfer learning-based approach achieves comparable or better performance compared to fully calibrated approaches in different transfer directions.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Chemistry, Analytical
Yeou-Jiunn Chen, Pei-Chung Chen, Shih-Chung Chen, Chung-Min Wu
Summary: A noise suppression-based feature extraction and deep neural network approach has been proposed to develop a robust SSVEP-based BCI, which effectively suppresses noise and greatly improves the performance of SSVEP-based BCIs for practical applications.
Article
Engineering, Biomedical
Yufeng Ke, Jiale Du, Shuang Liu, Dong Ming
Summary: This study proposed a novel FS algorithm framework for enhanced control state detection in high-performance asynchronous SSVEP-BCIs. The FS framework incorporates TRCA-based SSVEP identification and multiple FS control state detection classifiers. Offline evaluation found that the FS framework outperformed a FU framework. Through online experiments, the FS system achieved significantly higher performance and reliability compared to the FU system.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Editorial Material
Automation & Control Systems
Rodolphe Sepulchre
Summary: Deep learning has had a significant impact on control, reestablishing the importance of state-space concept and becoming a common language among researchers and scientists in various fields.
IEEE CONTROL SYSTEMS MAGAZINE
(2022)
Article
Engineering, Biomedical
Jiayang Huang, Zhi-Qiang Zhang, Bang Xiong, Quan Wang, Bo Wan, Fengqi Li, Pengfei Yang
Summary: This study proposed a cross-subject transfer method based on domain generalization to transfer the domain-invariant spatial filters and templates from source subjects to target subjects. The transferred filters and templates were obtained by maximizing the intra- and inter-subject correlations using SSVEP data. Experimental results showed that the proposed method improved the SSVEP detection performance compared to state-of-the-art methods, providing an effective transfer learning strategy for practical applications of SSVEP-based BCI.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Review
Oncology
Arturo Kenzuke Nakamura-Garcia, Jesus Espinal-Enriquez
Summary: Pseudogenes are duplicate genes that have accumulated detrimental alterations and are unable to produce functional proteins. Initially considered as junk DNA, recent research has focused on their abnormal expression in cancer. This review aims to provide a comprehensive overview of pseudogene formation, expression regulation mechanisms, and their potential roles in promoting tumorigenesis.
Article
Health Care Sciences & Services
Shouwei Gao, Kang Zhou, Jun Zhang, Yi Cheng, Shujun Mao
Summary: This study investigates the compensatory effect of music on mental fatigue in long-duration, SSVEP-based BCI tasks. The results show that introducing exciting background music can effectively relieve participants' mental fatigue. In addition, using soothing background music during the rest interval phase is more effective in reducing users' mental fatigue for continuous SSVEP-BCI tasks, suggesting that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.
Review
Radiology, Nuclear Medicine & Medical Imaging
Akinori Hata, Mark L. Schiebler, David A. Lynch, Hiroto Hatabu
Summary: This article summarizes the definition, evidence, clinical management, and unresolved issues of interstitial lung abnormality (ILA) and provides a practical guide for radiologists. ILA is a common finding on CT scans and can lead to worsened clinical outcomes. Radiologists should systematically document ILA characteristics to predict progression and mortality risks.
Review
Biochemistry & Molecular Biology
Grzegorz Procyk, Dominik Bilicki, Pawel Balsam, Piotr Lodzinski, Marcin Grabowski, Aleksandra Gasecka
Summary: This review thoroughly summarizes current research on extracellular vesicles in atrial fibrillation, pointing out that patients with atrial fibrillation show altered levels of vesicles and their contents, but further clinical trials are needed to validate previous findings.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Thermodynamics
Inderjot Kaur, Prashant Singh
Summary: The progress in additive manufacturing has revolutionized heat exchanger fabrication, offering benefits in weight, volume, load bearing capabilities, and cost reduction. However, challenges such as process parameters, surface quality, and material choice accompany these advantages. Exciting times lie ahead in heat exchanger development with continuous improvements in AM technologies.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Review
Chemistry, Multidisciplinary
Tianjian Zhang, Changhui Liu, Yanlong Gu, Francois Jerome
Summary: Glycerol, with its abundant feedstock and unique properties, is an ideal candidate for catalysis, high-value-added chemical conversion, and energy transportation. Recent advances in the utilization of glycerol in energy transportation were summarized, showcasing its potential in industrial processes.
Article
Engineering, Biomedical
Rui Bian, Huanyu Wu, Bin Liu, Dongrui Wu
Summary: This paper introduces a quick calibration method called sd-LST to improve the performance of SSVEP-based BCIs. Experiments show that sd-LST outperforms other methods on multiple publicly available datasets.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Review
Chemistry, Multidisciplinary
Roja Hadianamrei, Xiubo Zhao
Summary: Gene therapy involves correcting specific pathological conditions by introducing exogenous genetic materials into cells. However, efficient delivery of these materials is hindered by various barriers, necessitating the use of gene vectors. Peptides have emerged as promising biomaterials for gene delivery due to their desirable properties. This review provides an overview of recent advances in peptide-based gene delivery systems and focuses on systems composed solely of peptides.
JOURNAL OF CONTROLLED RELEASE
(2022)
Review
Construction & Building Technology
Vladimir Vukobratovic, Sergio Ruggieri
Summary: This paper introduces the applications and research progress of jerk in earthquake engineering, and points out the lack of a comprehensive overview of the literature related to jerk.
Article
Computer Science, Artificial Intelligence
Vignesh Srinivasan, Klaus-Robert Mueller, Wojciech Samek, Shinichi Nakajima
Summary: In this article, the authors propose a strategy called Langevin cooling (L-Cool) to enhance existing methods in image translation and language translation tasks. They suggest using Langevin dynamics to bring fringe samples from low-density areas to high-density areas.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Biochemistry & Molecular Biology
Philipp Keyl, Philip Bischoff, Gabriel Dernbach, Michael Bockmayr, Rebecca Fritz, David Horst, Nils Bluethgen, Gregoire Montavon, Klaus-Robert Mueller, Frederick Klauschen
Summary: The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. Single-cell sequencing has limitations in understanding this heterogeneity, and scGeneRAI is an explainable deep learning approach that can infer gene regulatory networks from single-cell RNA sequencing data to provide functional insights. Our method reveals characteristic network patterns for tumor cells and normal epithelial cells and allows the reconstruction of networks at the level of single cells, which helps characterize the heterogeneity of gene regulation within and across tumors.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Multidisciplinary Sciences
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Mueller
Summary: We have developed an exact iterative approach to train global symmetric gradient domain machine learning (sGDML) force fields, which can accurately describe complex molecular systems and materials. We evaluated the accuracy and efficiency of sGDML on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms.
Article
Chemistry, Physical
Stefan Bluecher, Klaus-Robert Mueller, Stefan Chmiela
Summary: Kernel machines have achieved continuous progress in the field of quantum chemistry, especially in the low-data regime of force field reconstruction. However, the scalability of kernel machines has been hindered by their quadratic memory and cubical runtime complexity.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Multidisciplinary Sciences
Thibault Porssut, Fumiaki Iwane, Ricardo Chavarriaga, Olaf Blanke, Jose del R. Millan, Ronan Boulic, Bruno Herbelin
Summary: The brain's reaction to a disruption in embodiment, termed Breaks in Embodiment (BiE), was investigated using electroencephalography (EEG). The study found that error-related potentials were observed during the monitoring step when participants experienced a BiE event. Importantly, the EEG signature showed amplified potentials following a non-embodied condition, indicating an accumulation of errors across steps. These neurophysiological findings provide insights into how progressive disruptions impact the expectation of embodiment in a virtual body.
Article
Mathematics
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Mueller
Summary: The paper introduces an efficient exact inference method for local models, which allows for finer interactions between the energy of the core model and the sufficient statistics of the global terms. This greatly increases the range of admissible applications and improves upon the theoretical guarantees of computational efficiency.
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Chemistry, Physical
Joshua Scheidt, Alexander Diener, Michael Maiworm, Klaus-Robert Mueller, Rolf Findeisen, Kurt Driessens, F. Stefan Tautz, Christian Wagner
Summary: A nanofabrication technique involving the assembly of functional molecular structures using a scanning probe microscope (SPM) has been developed. The key challenge was the lack of simultaneous actuation and imaging capabilities of the SPM tip, which hindered continuous monitoring of molecular configuration during manipulation. However, in this study, configuration monitoring was achieved through modelling the manipulation process as a partially observable Markov decision process (POMDP) and using a particle filter. The proposed methodology is an important step towards the robotic and possibly automated creation of supramolecular structures.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Chemistry, Physical
Bipeng Wang, Ludwig Winkler, Yifan Wu, Klaus-Robert Muller, Huziel E. Sauceda, Oleg V. Prezhdo
Summary: Nonadiabatic molecular dynamics (MD) is crucial for understanding far-from-equilibrium processes, but requires expensive calculations of excitation energies and nonadiabatic couplings. In this study, a bidirectional long short-term memory network (Bi-LSTM) is employed in the time domain to interpolate the Hamiltonian, achieving significant computational savings compared to direct ab initio calculation. The Bi-LSTM-NAMD method outperforms previous models and captures slow and fast time scales, extending MD simulation times from picoseconds to nanoseconds.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Automation & Control Systems
Armin W. Thomas, Ulman Lindenberger, Wojciech Samek, Klaus-Robert Mueller
Summary: Research has shown that transfer learning improves the performance of deep learning models in datasets with small sample sizes. In this study, the application of transfer learning to cognitive decoding analysis using functional neuroimaging data is systematically evaluated. Pre-trained deep learning models consistently achieve higher decoding accuracies and require less training time and data compared to models trained from scratch. The benefits of pre-training come from the ability to reuse learned features when training with new data. However, challenges arise when interpreting the decoding decisions of pre-trained models, as they may utilize fMRI data in unforeseen and counterintuitive ways.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Frigyes Samuel Racz, Rawan Fakhreddine, Satyam Kumar, Jose del R. Millan
Summary: In this study, a novel decoding algorithm utilizing Riemannian geometry, template matching and adaptive re-centering was proposed and evaluated for single-trial detection of slow cortical potentials. The results showed that the algorithm can efficiently detect these signals in online applications.
2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Ruofan Liu, Satyam Kumar, Hussein Alawieh, Evan Carnahan, Jose del R. Millan
Summary: To accurately decode motor intentions, motor imagery-based brain-computer interfaces typically require subject-specific calibration data. This paper proposes a geometry-aware deep learning architecture that exploits the spatial similarity of motor imagery neural activity between users. The results show that the proposed method outperforms classical decoding algorithms in a subject-specific setting and achieves similar performance to subject-specific decoders in a transfer learning setting.
2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER
(2023)
Proceedings Paper
Automation & Control Systems
Gloria Beraldo, Luca Tonin, Amedeo Cesta, Emanuele Menegatti, Jose del R. Millan
Summary: Telepresence robots can help people with special needs interact with people and the environment remotely, but the accuracy of alternative communication channels such as brain-machine interfaces is lower. This study compared the navigation performance of a brain-machine interface with a keyboard interface and found similar results, but differences in user inclination were observed in different navigation situations. It suggests the need to adapt the shared intelligence system according to the real-time user's ability and the surrounding environment.
INTELLIGENT AUTONOMOUS SYSTEMS 17, IAS-17
(2023)
Article
Computer Science, Artificial Intelligence
Ping-Keng Jao, Ricardo Chavarriaga, Jose del R. Millan
Summary: The study found that adaptively increasing the difficulty level based on subjective perception through decoding EEG signals is more beneficial than increasing the level at fixed time intervals. To investigate the effectiveness of the EEG decoder, a visuomotor learning task was designed to pilot a simulated drone through waypoints of different sizes. The EEG decoder was compared with a condition where subjects manually regulated the difficulty level. The decoding performance of EEG condition was higher than chance level in 16 out of 26 cases, and the behavioral results were similar to the manual regulation condition.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Psychology, Educational
Amna Ghani, Caroline Di Bernardi Luft, Smadar Ovadio-Caro, Klaus-Robert Mueller, Joydeep Bhattacharya
Summary: Chance favors the prepared mind, said Louis Pasteur. In this study, the researchers investigated the brain's receptivity to integrate new information and the experience of creative insights known as Aha! moments. They hypothesized that the transient oscillatory states of the brain would characterize its preparedness for these insights. Through a real-time brain-state-dependent cognitive stimulation experiment, they found that participants were more successful in utilizing clues and experienced more Aha responses when clues were presented at the up-regulated state of right temporal alpha oscillation. Additionally, they observed a negative correlation between the coupling of alpha oscillation phase and gamma oscillation power and the frequency of Aha moments. These findings highlight the role of brain oscillations in the Aha experience and provide insights into the neural mechanism underlying the brain's receptivity to integrate semantic information.
CREATIVITY RESEARCH JOURNAL
(2023)
