Article
Automation & Control Systems
Wei Wu, Wei Sun, Q. M. Jonathan Wu, Yimin Yang, Hui Zhang, Wei-Long Zheng, Bao-Liang Lu
Summary: This article discusses the phenomenon of increasing accidents caused by reduced vigilance and proposes a method based on a multimodal regression network with feature fusion to improve accuracy and efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Electrical & Electronic
Jiahui Pan, Xugang Cai, Danying Mo, Yangzuyi Yu, Yuanqing Li
Summary: Driver vigilance estimation is crucial for reducing fatigue and traffic accidents. This article proposes a multimodal detection method that utilizes residual attention network and capsule attention mechanism to optimize features and explore part-whole relationships, thereby improving the performance of the neural network.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Biomedical
Kangning Wang, Shuang Qiu, Wei Wei, Weibo Yi, Huiguang He, Minpeng Xu, Tzyy-Ping Jung, Dong Ming
Summary: This study investigates the vigilance levels in brain-computer interface (BCI) tasks by analyzing EEG patterns and performances. The results show that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels and BCI tasks, with the theta frequency band features playing a critical role in vigilance estimation. This study provides a foundation for further research in vigilance estimation in BCI applications.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Ximing Mai, Xinjun Sheng, Xiaokang Shu, Yidan Ding, Xiangyang Zhu, Jianjun Meng
Summary: The study proposes a novel calibration-free Bayesian approach for a brain-computer interface (BCI) by hybridizing SSVEP and electrooculography (EOG). The method successfully enables continuous control of external devices, with significantly improved accuracy and gaze-shifting time compared to existing approaches. This research provides a valuable framework for the development and application of plug-and-play BCIs in continuous control.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Tiehang Duan, Zhenyi Wang, Sheng Liu, Yiyi Yin, Sargur N. Srihari
Summary: EEG decoding systems based on deep neural networks are widely used in brain-computer interfaces for decision-making. However, the reliability of their predictions is affected by the variance and noise in EEG signals. Previous studies have focused on noise patterns in the source signal, neglecting the uncertainty during the decoding process. This work proposes an uncertainty estimation and reduction model (UNCER) that incorporates dropout and Bayesian neural network methods to quantify and mitigate decoding uncertainty. The model achieves significant improvement in uncertainty estimation and reduction in motor imagery applications.
Article
Engineering, Biomedical
Yukun Zhang, Shuang Qiu, Huiguang He
Summary: A multimodal decoding neural network was proposed in this study to enhance the decoding accuracy of motor imagery-based brain-computer interfaces (MI-BCIs) by aligning and fusing features from different modalities. Experimental results showed that the proposed method achieved higher decoding accuracy than compared methods, and feature distribution visualization results demonstrated the effectiveness of the proposed losses in enhancing feature representations. The proposed method based on EEG and fNIRS modalities has significant potential for improving decoding performance of MI tasks.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Ben McCartney, Barry Devereux, Jesus Martinez-del-Rincon
Summary: This paper proposes a deep learning based approach for image retrieval using EEG, which utilizes a multi-modal deep neural network and metric learning to map EEG signals and visual information. With the scalable metric learning approach, the system achieves zero-shot image retrieval with new images and demonstrates state-of-the-art results on standard EEG image-viewing datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematical & Computational Biology
Gan Wang, Moran Cerf
Summary: This study demonstrates the effectiveness of using deep learning neural networks to improve the performance of brain-computer interfaces in predicting imagined motor actions. By extracting temporal and spectral features from EEG signals, the algorithm achieved an average performance increase of 3.50% compared to benchmark algorithms. The results on public datasets also showed high accuracy rates.
FRONTIERS IN NEUROINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Kangning Wang, Shuang Qiu, Wei Wei, Yukun Zhang, Shengpei Wang, Huiguang He, Minpeng Xu, Tzyy-Ping Jung, Dong Ming
Summary: In this study, a 4-target BCI system based on SSVEP was built to assist people with disabilities. EEG and EOG data were recorded from 18 subjects during a 90-min continuous task, and a multimodal vigilance estimating network, MVENet, was proposed to estimate the vigilance state of BCI users. Experimental results showed that the network achieved better performance than the compared methods, demonstrating the feasibility and effectiveness of our methods for estimating the vigilance state of BCI users.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Psychology, Applied
C. Giot, M. Hay, C. Chesneau, E. Pigeon, T. Bonargent, M. Beaufils, N. Chastan, J. Perrier, F. Pasquier, S. Polvent, D. Davenne, J. Taillard, N. Bessot
Summary: The Objective Sleepiness Scale (OSS) was tested for reliability and time synchronization in detecting sleepiness, showing higher scores in sleep deprivation conditions and correlation with driving and vigilance task performance.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2022)
Article
Engineering, Biomedical
Ziwen Yuan, Yu Peng, Lisha Wang, Siming Song, Shi Chen, Liu Yang, Huanhuan Liu, Haochong Wang, Gaige Shi, Chengcheng Han, Jared A. Cammon, Yingchun Zhang, Jin Qiao, Gang Wang
Summary: BCI-PT significantly improved stroke patients' lower limb motor function by increasing patient participation, as shown in a randomized controlled clinical trial.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Dmitry Lazurenko, Igor Shepelev, Dmitry Shaposhnikov, Anton Saevskiy, Valery Kiroy
Summary: This study presents a linear discriminant analysis transformation-based approach for classifying three different types of motor imagery in brain-computer interfaces. The results demonstrate that this method improves classification accuracy and successfully discriminates two out of three pairs of motor imagery.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Biomedical
Xiaolin Xiao, Lichao Xu, Jin Yue, Baizhou Pan, Minpeng Xu, Dong Ming
Summary: This study proposed a novel network design based on decomposition methods and demonstrated its potential in SSVEP decoding tasks through comparison with state-of-the-art decomposition methods.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yanzheng Lu, Hong Wang, Naishi Feng, Daqi Jiang, Chunfeng Wei
Summary: This paper proposes an end-to-end CNN model to control a robot using head signals. By analyzing the eye signals, actions such as blinking and gritting teeth can be detected, and an online brain-computer interface system is developed for interaction with the robot.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Cybernetics
Nathan Semertzidis, Fabio Zambetta, Florian Floyd Mueller
Summary: This article proposes a paradigm of human-computer integration, going beyond traditional interaction approaches in BCI system design. Three prototypes are presented to demonstrate the potential of this paradigm. Studies show the advantages of brain-computer integration in realizing the multifaceted benefits of BCI systems, and a framework is provided to guide future BCI integration designs.
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION
(2023)
Article
Clinical Neurology
Jennifer A. Kim, Wei-Long Zheng, Jonathan Elmer, Jin Jing, Sahar F. Zafar, Manohar Ghanta, Valdery Moura Junior, Emily J. Gilmore, Lawrence J. Hirsch, Aman Patel, Eric Rosenthal, Brandon M. Westover
Summary: This study aims to investigate whether epileptiform discharge burden can identify individuals at risk for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH). A retrospective analysis was conducted on 113 SAH patients with moderate to severe grade who had continuous EEG (cEEG) recordings during hospitalization. The burden of epileptiform discharges (ED), measured as the number of ED per hour, was calculated. The results indicate that ED burden is a useful parameter for identifying individuals at higher risk of developing DCI after SAH, and specific trends of ED burden over time can help stratify DCI risk.
CLINICAL NEUROPHYSIOLOGY
(2022)
Article
Engineering, Biomedical
Xun Wu, Wei-Long Zheng, Ziyi Li, Bao-Liang Lu
Summary: This study proposes a novel algorithm for selecting emotion-relevant critical subnetworks and investigates three EEG functional connectivity network features. The results show that these EEG connectivity features achieve high classification accuracy in emotion recognition.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Clinical Neurology
Edilberto Amorim, Marcos S. Firme, Wei-Long Zheng, Kenneth T. Shelton, Oluwaseun Akeju, Gaston Cudemus, Raz Yuval, M. Brandon Westover
Summary: This study retrospectively reviewed ECMO patients from 2011-2018 in a university-affiliated academic hospital, and found that seizures and ictal-interictal continuum patterns are relatively common during ECMO, but their association with in-hospital mortality and poor neurological outcomes is not significant.
CLINICAL NEUROPHYSIOLOGY
(2022)
Article
Engineering, Biomedical
Yong Peng, Honggang Liu, Junhua Li, Jun Huang, Bao-Liang Lu, Wanzeng Kong
Summary: This paper proposes a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition. The experimental results demonstrate that JCSFE achieves superior emotion recognition performance and provides a quantitative method to identify the label-common and label-specific EEG features in emotion recognition.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Yingying Zhang, Xi Luo, Longfei Yin, Fengwei Yin, Weilong Zheng, Yongqian Fu
Summary: Chitosan, a biopolymer extracted from marine biomass waste, has potential applications in wastewater treatment and soil remediation due to its biocompatibility and degradability. Improved adsorption performance of chitosan is achieved with a higher degree of deacetylation. A natural chitin degrading bacterium, Bacillus cereus ZWT-08, was identified and its enzyme production was optimized for higher deacetylation activity. This strain plays a positive role in the bioconversion of chitin and the development of the chitosan industry.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Automation & Control Systems
Yong Peng, Honggang Liu, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki
Summary: In this article, a joint EEG feature transfer and semi-supervised cross-subject emotion recognition model is proposed to enhance emotion recognition performance by optimizing the shared subspace projection matrix and target label. The spatial-frequency activation patterns of critical EEG frequency bands and brain regions in cross-subject emotion expression are quantitatively identified by analyzing the learned shared subspace.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Biomedical
Dan Peng, Wei-Long Zheng, Luyu Liu, Wei-Bang Jiang, Ziyi Li, Yong Lu, Bao-Liang Lu
Summary: This paper explores the sex differences in emotional EEG patterns and finds that these differences exist in various types of emotions and different cultures. Females have more stable emotional patterns compared to males, and males exhibit contrasting patterns for happiness, sadness, fear, and disgust. The key features for emotion recognition are located in the frontal and temporal sites for females and more evenly distributed throughout the brain for males.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Clinical Neurology
Edilberto Amorim, Wei-Long Zheng, Jin Jing, Mohammad M. Ghassemi, Jong Woo Lee, Ona Wu, Susan T. Herman, Trudy Pang, Adithya Sivaraju, Nicolas Gaspard, Lawrence Hirsch, Barry J. Ruijter, Marleen C. Tjepkema-Cloostermans, Jeannette Hofmeijer, Michel J. A. M. van Putten, M. Brandon Westover
Summary: This study aims to investigate the evolution of neurophysiology features associated with recovery from coma after cardiac arrest. Using a combination of EEG features, the study defines different neurophysiology states and finds that transition to high entropy states is associated with good recovery outcomes.
Article
Engineering, Electrical & Electronic
Dongrui Wu, Bao-Liang Lu, Bin Hu, Zhigang Zeng
Summary: A brain-computer interface (BCI) allows direct communication between a user and a computer through the central nervous system. An affective BCI (aBCI) monitors and regulates the emotional state of the brain, which has various applications in human cognition, communication, decision-making, and health. This tutorial provides a comprehensive and up-to-date guide on aBCIs, covering basic concepts, components of a closed-loop aBCI system, representative applications, and challenges and opportunities in aBCI research and applications.
PROCEEDINGS OF THE IEEE
(2023)
Article
Computer Science, Artificial Intelligence
Yong Peng, Keding Chen, Feiping Nie, Bao-Liang Lu, Wanzeng Kong
Summary: This study proposes a novel Fuzzy k-means (FKM) method called two-dimensional embedded fuzzy data clustering (2DEFC), which retains structural information and optimizes the projection matrices of two subspaces collaboratively. By optimizing the input and clustering processes for 2D data, competitive performance in 2D data clustering is achieved.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yong Peng, Wenna Huang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu
Summary: Most existing graph-based clustering models adopt a two-stage strategy for clustering, which first completes spectral embedding from a fixed graph and then uses other clustering methods such as k-means to obtain discrete cluster results. However, this discretization operation often leads to deviations from the true solution and the fixed graph is usually suboptimal. Additionally, clustering in separate steps breaks the underlying connections among graph construction, spectral embedding, and discretization. To address these issues, we propose JGSED, a new spectral clustering model that integrates graph construction, spectral embedding, and spectral rotation into a unified objective. JGSED is an end-to-end framework that directly takes data as input and outputs the final binary cluster indicator matrix. An efficient algorithm is proposed to optimize the model variables in JGSED, leading to improved performance compared to other state-of-the-art spectral clustering models.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yong Peng, Wenjuan Wang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki
Summary: In this paper, a joint feature adaptation and graph adaptive label propagation model (JAGP) is proposed for cross-subject emotion recognition from EEG signals. By unifying the previously scattered feature learning, emotional state estimation, and optimal graph learning into a single objective, the recognition performance is greatly improved, and the critical frequency bands and brain regions can be automatically identified.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Yikai Zhang, Ruiqi Guo, Yong Peng, Wanzeng Kong, Feiping Nie, Bao-Liang Lu
Summary: This study proposes a new model AWIRVFL for EEG-based driving fatigue detection, which addresses the limitations of existing methods. The AWIRVFL model incorporates an auto-weighting variable to consider the importance of different feature dimensions. Experimental results demonstrate that AWIRVFL outperforms existing techniques in driving fatigue detection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Clinical Neurology
Nitish M. Harid, Jin Jing, Jacob Hogan, Fabio A. Nascimento, An Ouyang, Wei-Long Zheng, Wendong Ge, Sahar F. Zafar, Jennifer A. Kim, D. Lam Alice, Aline Herlopian, Douglas Maus, Ioannis Karakis, Marcus Ng, Shenda Hong, Zhu Yu, Peter W. Kaplan, Sydney Cash, Mouhsin Shafi, Gabriel Martz, Jonathan J. Halford, Michael Brandon Westover
Summary: This study developed a test to quantify readers' skills in identifying interictal epileptiform discharges (IEDs) on EEGs. The results showed that expert and non-expert readers can be distinguished based on their ability to identify IEDs, and this test could also be used to identify and correct differences in thresholds in identifying IEDs.
EPILEPTIC DISORDERS
(2022)