Article
Biology
Binish Fatimah, Amit Singhal, Pushpendra Singh
Summary: Healthy sleep is crucial for the body's rejuvenation and overall health. Automated assessment of sleep disorders using EEG and other signals can improve classification accuracy. The proposed method allows for real-time and cost-effective continuous patient monitoring and feedback.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biotechnology & Applied Microbiology
Md Shafayet Hossain, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, Zaid Bin Mahbub, Mamun Bin Ibne Reaz, Muhammad E. H. Chowdhury
Summary: This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) called MultiResUNet3+ to remove physiological artifacts from electroencephalogram (EEG) signals. A publicly available dataset is used to train, validate, and test the proposed model along with four other 1D-CNN models. The results show that MultiResUNet3+ achieves the highest reduction in EOG and EMG artifacts compared to the other models.
BIOENGINEERING-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Shiqi Yang, Min Li, Jiale Wang
Summary: This article proposes a multimodal fusion strategy of EEG and sEMG based on graph theory to improve the accuracy and robustness of hand motion recognition. By considering the temporal signals of EEG and sEMG as the features of nodes and the functional connectivity as the weights of edges, the proposed approach achieves significantly higher accuracy than parallel fusion and single-modality models under abnormal states such as muscular fatigue and weakness.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Analytical
Chunghwan Kim, Ho-Seung Cha, Junghwan Kim, HwyKuen Kwak, WooJin Lee, Chang-Hwan Im
Summary: With the rapid development of VR technology and the growth of SNS, VR-based SNS with 3D avatars have been actively developed. However, traditional camera-based facial recognition is difficult for VR users due to occlusion by the HMD. To address this, facial expressions are recognized using fEMG recorded around the eyes. In this study, an fEMG- and EOG-based FER system was implemented to capture facial motions and replicate them on a 3D avatar face in real time.
Review
Clinical Neurology
Jonathan J. Halford, David A. Clunie, Benjamin H. Brinkmann, Dagmar Krefting, Jan Remi, Felix Rosenow, Aatif Husain, Franz Furbass, J. Andrew Ehrenberg, Silvia Winkler
Summary: The urgent need for a standard format for neurophysiology data to improve clinical care and promote research data exchange is highlighted. The widely adopted DICOM standard in medical imaging offers a unique environment for neurophysiology format standardization, with the support of the IFCN and industry partners.
CLINICAL NEUROPHYSIOLOGY
(2021)
Article
Neurosciences
Chao-Lin Teng, Yi-Yang Zhang, Wei Wang, Yuan-Yuan Luo, Gang Wang, Jin Xu
Summary: The proposed EEMD-based ICA method (EICA) effectively removes EOG artifacts from multichannel EEG signals by combining EEMD and ICA algorithms. It achieves the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal compared to other existing approaches, demonstrating its superior performance in eliminating blink artifacts. This study provides a novel promising method for high-performance elimination of EOG artifacts in EEG signals processing and analysis.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Ruchi Juyal, Hariharan Muthusamy, Niraj Kumar
Summary: This study proposes a new method for removing ocular artifacts from multi-channel EEG signals using singular spectrum analysis (SSA) and non-negative matrix factorization (NMF). The results show that the proposed method achieves better performance in artifact removal compared to other existing methods.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Engineering, Biomedical
Jingyao Sun, Tianyu Jia, Zhibin Li, Chong Li, Linhong Ji
Summary: In this study, the concept of spatial-temporal CMC (STCMC) is proposed to improve the accuracy of corticomuscular coupling analysis. By combining delay compensation and spatial optimization, STCMC enhances the coherence significantly between brain and muscle signals and produces higher classification accuracy. Furthermore, STCMC provides more detailed brain topographical patterns, emphasizing the different roles between the contralateral and ipsilateral hemisphere.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Chemistry, Analytical
Ajay Kumar Maddirala, Kalyana C. Veluvolu
Summary: The use of portable electroencephalogram (EEG) devices has increased in recent years for recording brain signals in healthcare monitoring and other applications. However, the measured EEG signals often contain artifacts from eyelid blinking or eye movements, which mislead the understanding of the brain state. Traditional artifact removal techniques cannot be applied to single-channel EEG signals, calling for new techniques. In this paper, a method combining singular spectrum analysis (SSA), continuous wavelet transform (CWT), and k-means clustering algorithm is proposed to remove eye-blink artifacts from single-channel EEG signals without affecting the low frequencies.
Article
Biology
Yonglin Wu, Xinyu Jiang, Yao Guo, Hangyu Zhu, Chenyun Dai, Wei Chen
Summary: This study aims to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. The study reaches a more general conclusion on modality comparison and fusion based on the regression of features or their combinations and the awake-to-drowsy transition. The most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Jammisetty Yedukondalu, Lakhan Dev Sharma
Summary: This study presents an automated method for removing electrooculogram (EOG) artifacts from electroencephalogram (EEG) signals. The approach decomposes the contaminated signals into intrinsic mode functions (IMFs) using Circulant Singular Spectrum Analysis (CiSSA) and removes the artifact components using 4-level discrete wavelet transform (DWT). The proposed technique effectively eliminates EOG artifacts while preserving low-frequency EEG information.
Article
Biology
Manish Sharma, Jay Darji, Madhav Thakrar, U. Rajendra Acharya
Summary: Sleep is crucial for a healthy life, but sleep disorders can cause various issues. Traditional sleep monitoring methods are complex and not suitable for real-time monitoring, so there is a need for a simple and convenient system to monitor sleep quality. This research proposes an automatic sleep disorder detection method using electrooculogram (EOG) and electromyogram (EMG) signals, and develops a model using machine learning classifiers. The results show that the proposed method can accurately classify different types of sleep disorders.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Multidisciplinary Sciences
Bernd Porr, Sama Daryanavard, Lucia Munoz Bohollo, Henry Cowan, Ravinder Dahiya
Summary: This article introduces a new real-time deep learning algorithm that effectively reduces non-stationary noise in biological measurements. By removing electromyogram noise, the algorithm significantly improves the signal-to-noise ratio of electroencephalograms. This concept has the potential to be applied not only to EEG, but also to various biological, industrial, and consumer applications.
Article
Biology
Nesma E. ElSayed, A. S. Tolba, M. Z. Rashad, Tamer Belal, Shahenda Sarhan
Summary: A method to improve the efficiency of correcting EOG artifacts in raw EEG recordings is introduced in this study, utilizing feature engineering and machine learning to extract brain information and remove artifacts, with different classification models and regression learners to enhance accuracy in classification and prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
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
Mathematical & Computational Biology
Jinhua Zhang, Baozeng Wang, Cheng Zhang, Jun Hong
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2016)
Article
Mathematical & Computational Biology
Ting Li, Jinhua Zhang, Tao Xue, Baozeng Wang
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2017)
Article
Instruments & Instrumentation
Jinhua Zhang, Baozeng Wang, Ting Li, Jun Hong
REVIEW OF SCIENTIFIC INSTRUMENTS
(2018)
Article
Biotechnology & Applied Microbiology
Jinhua Zhang, Jiongjian Wei, Baozeng Wang, Jun Hong, Jing Wang
BIOMED RESEARCH INTERNATIONAL
(2014)
Article
Neurosciences
Ting Li, Tao Xue, Baozeng Wang, Jinhua Zhang
FRONTIERS IN HUMAN NEUROSCIENCE
(2018)
Article
Neurosciences
Pengna Wei, Jinhua Zhang, Baozeng Wang, Jun Hong
Summary: The study utilized the SSC and MPF features of EEG and sEMG to recognize seven gait phases, providing a theoretical foundation for the control systems of lower limb exoskeletons for rehabilitation. The time-frequency cross mutual information method revealed differences in the corticomuscular interaction at different gait phases, enhancing previous findings and contributing to the understanding of gait recognition based on EEG and sEMG.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Engineering, Multidisciplinary
Zhang HuaLian, Liu Ji, Wang BaoZeng, Dai Jun, Lian JinLing, Ke Ang, Zhao YuWei, Zhou Jin, Wang ChangYong
Summary: In this study, a micro Capsnet network architecture is proposed to improve the decoding accuracy of neural signals by reducing computing time and complexity. Compared with other algorithms, this approach achieves significantly higher decoding accuracy and stronger robustness. Additionally, it helps enhance the control accuracy of brain-computer interfaces.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Proceedings Paper
Engineering, Biomedical
Pengna Wei, Jinhua Zhang, Pingping Wei, Baozeng Wang, Jun Hong
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Linhua Zhang, Baozeng Wang, Jun Hong, Ting Li
2015 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Jinhua Zhang, Baozeng Wang, Jun Hong, Ting Li, Feng Guo
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2014, PT I
(2014)
