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Xu X, Zhu G, Li B, Lin P, Li X, Wang Z. Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network. Biomed Eng Online 2024; 23:55. [PMID: 38886737 PMCID: PMC11181588 DOI: 10.1186/s12938-024-01250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.
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Affiliation(s)
- Xinyi Xu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu Mental Health Center, Shanghai, China
| | - Ping Lin
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Shanghai Yangpu Mental Health Center, Shanghai, China.
| | - Zhen Wang
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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2
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Qian D, Zeng H, Cheng W, Liu Y, Bikki T, Pan J. NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108213. [PMID: 38744056 DOI: 10.1016/j.cmpb.2024.108213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND AND OBJECTIVE Brain-Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity. METHODS In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features. RESULTS We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods. CONCLUSIONS The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.
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Affiliation(s)
- Dongguan Qian
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Hong Zeng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Wenjie Cheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Yu Liu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Taha Bikki
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Jianjiang Pan
- School of Sciences, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
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3
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Baghernezhad S, Daliri MR. Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data. GeroScience 2024:10.1007/s11357-024-01128-w. [PMID: 38499956 DOI: 10.1007/s11357-024-01128-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.
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Affiliation(s)
- Sepideh Baghernezhad
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
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4
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Cao J, Yang L, Sarrigiannis PG, Blackburn D, Zhao Y. Dementia classification using a graph neural network on imaging of effective brain connectivity. Comput Biol Med 2024; 168:107701. [PMID: 37984205 DOI: 10.1016/j.compbiomed.2023.107701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/16/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023]
Abstract
Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK; School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Lichao Yang
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK
| | | | - Daniel Blackburn
- Department of Neurosciences, Sheffield Teaching Hospitals, NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, UK
| | - Yifan Zhao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK.
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5
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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6
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Leong D, Do TTT, Lin CT. Distinction of the object recognition and object identification in the brain-computer interfaces applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083669 DOI: 10.1109/embc40787.2023.10340753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Object recognition is a complex cognitive process in which information is integrated and processed by various brain regions. Previous studies have shown that both the visual and temporal cortices are active during object recognition and identification. However, although object recognition and object identification are similar, these processes are considered distinct functions in the brain. Despite this, the differentiation between object recognition and identification has yet to be clearly defined for use in brain-computer interface (BCI) applications. This research aims to utilize neural features related to object recognition and identification and classify these features to differentiate between the two processes. The results demonstrate that several classifiers achieved high levels of accuracy, with the XGBoost classifier using a Linear Booster achieving the highest accuracy at 96% and a F1 score of 0.97. This ability to distinguish between object recognition and identification can be a beneficial aspect of a BCI object recognition system as it could help determine the intended target object for a user.
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Tian Z, Hu B, Si Y, Wang Q. Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier. Brain Sci 2023; 13:brainsci13050820. [PMID: 37239292 DOI: 10.3390/brainsci13050820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/28/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.
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Affiliation(s)
- Ziwei Tian
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 101408, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Bingliang Hu
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Yang Si
- Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu 610072, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
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8
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Liao PC, Zhou X, Chong HY, Hu Y, Zhang D. Exploring construction workers' brain connectivity during hazard recognition: a cognitive psychology perspective. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2023; 29:207-215. [PMID: 35098890 DOI: 10.1080/10803548.2022.2035966] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Monitoring brain activity is a novel development for hazard recognition in the construction industry. However, very few empirical studies have investigated the causal connections within the brain. This study aimed to explore the brain connectivity of construction workers during hazard recognition. Electroencephalogram data were collected from construction workers to perform image-based hazard recognition tasks. The Granger causality-based adaptive directed transfer function was used to simulate directed and time-variant information flow across the observed brain activity from the perspective of cognitive psychology. The results suggested a top-down modulation of behavioral goals originating from the dorsal attention network during hazard relocation. The sensory cortex predominantly serves as the information outlet center and interacts extensively with the frontal and visual cortices, reflecting a top-down attention reorientation mechanism for processing threatening stimuli. Our findings of brain effective connectivity supplement new evidence underpinning parallel distributed processing theory for workplace hazard recognition.
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Affiliation(s)
- Pin-Chao Liao
- Department of Construction Management, Tsinghua University, China
| | - Xiaoshan Zhou
- Department of Construction Management, Tsinghua University, China
| | - Heap-Yih Chong
- School of Design and the Built Environment, Curtin University, Australia
| | - Yinan Hu
- Department of Construction Management, Tsinghua University, China
| | - Dan Zhang
- Department of Psychology, Tsinghua University, China
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals. Cogn Neurodyn 2022; 16:1087-1106. [PMID: 36237402 PMCID: PMC9508317 DOI: 10.1007/s11571-021-09756-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/29/2021] [Accepted: 11/14/2021] [Indexed: 12/26/2022] Open
Abstract
Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.
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Affiliation(s)
- Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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10
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Cao J, Zhao Y, Shan X, Blackburn D, Wei J, Erkoyuncu JA, Chen L, Sarrigiannis PG. Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease. J Neural Eng 2022; 19. [PMID: 35896105 DOI: 10.1088/1741-2552/ac84ac] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 07/27/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram (EEG), a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD). APPROACH The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a Revised Hilbert-Huang Transformation cross-spectrum as a biomarker, the Support Vector Machine classifier is used to distinguish AD from healthy controls (HC). MAIN RESULTS With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. SIGNIFICANCE Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.
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Affiliation(s)
- Jun Cao
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Yifan Zhao
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Xiaocai Shan
- Cranfield University, Building 30, Cranfield, Bedford, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Daniel Blackburn
- Department of Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 7HQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Jize Wei
- Hong Kong Polytechnic University University Learning Hub, Department of Applied Mathematics, Kowloon, HONG KONG
| | - John Ahmet Erkoyuncu
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Liangyu Chen
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Sanhao street, Shenyang, 110004, CHINA
| | - Ptolemaios G Sarrigiannis
- Royal Devon and Exeter NHS Foundation Trust, 1, Exeter, EX2 5DW, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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11
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Shan X, Cao J, Huo S, Chen L, Sarrigiannis PG, Zhao Y. Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram. Hum Brain Mapp 2022; 43:5194-5209. [PMID: 35751844 PMCID: PMC9812255 DOI: 10.1002/hbm.25994] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/19/2022] [Accepted: 06/08/2022] [Indexed: 01/15/2023] Open
Abstract
Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.
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Affiliation(s)
- Xiaocai Shan
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina,School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
| | - Shoudong Huo
- Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
| | | | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfieldUK
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12
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Góngora L, Paglialonga A, Mastropietro A, Rizzo G, Barbieri R. A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. SENSORS 2022; 22:s22134747. [PMID: 35808250 PMCID: PMC9269473 DOI: 10.3390/s22134747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/05/2023]
Abstract
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.
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Affiliation(s)
- Leonardo Góngora
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Alessia Paglialonga
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (IEIIT), Consiglio Nazionale delle Ricerche (CNR), 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Giovanna Rizzo
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
- Correspondence:
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13
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Sun Y, Xu Y, Lv J, Liu Y. Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks. Neuropsychologia 2022; 173:108282. [PMID: 35660514 DOI: 10.1016/j.neuropsychologia.2022.108282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 05/13/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022]
Abstract
Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.
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Affiliation(s)
- Yan Sun
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Yuanyuan Xu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Jiaojiao Lv
- School of Psychology, Liaoning Normal University, Dalian, 116029, China; Department of Psychology, Shanxi Datong University, Datong, 037009, China
| | - Yan Liu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China.
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14
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EEG Connectivity during Active Emotional Musical Performance. SENSORS 2022; 22:s22114064. [PMID: 35684685 PMCID: PMC9185252 DOI: 10.3390/s22114064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/17/2022]
Abstract
The neural correlates of intentional emotion transfer by the music performer are not well investigated as the present-day research mainly focuses on the assessment of emotions evoked by music. In this study, we aim to determine whether EEG connectivity patterns can reflect differences in information exchange during emotional playing. The EEG data were recorded while subjects were performing a simple piano score with contrasting emotional intentions and evaluated the subjectively experienced success of emotion transfer. The brain connectivity patterns were assessed from the EEG data using the Granger Causality approach. The effective connectivity was analyzed in different frequency bands—delta, theta, alpha, beta, and gamma. The features that (1) were able to discriminate between the neutral baseline and the emotional playing and (2) were shared across conditions, were used for further comparison. The low frequency bands—delta, theta, alpha—showed a limited number of connections (4 to 6) contributing to the discrimination between the emotional playing conditions. In contrast, a dense pattern of connections between regions that was able to discriminate between conditions (30 to 38) was observed in beta and gamma frequency ranges. The current study demonstrates that EEG-based connectivity in beta and gamma frequency ranges can effectively reflect the state of the networks involved in the emotional transfer through musical performance, whereas utility of the low frequency bands (delta, theta, alpha) remains questionable.
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15
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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16
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Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method. SENSORS 2022; 22:s22093260. [PMID: 35590948 PMCID: PMC9102674 DOI: 10.3390/s22093260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 02/01/2023]
Abstract
With the rapid development of underground infrastructure and the uncertainty of its location, the possibility of damage due to nearby construction has increased. Thus, for the early warning of dangerous construction behaviors around underground facilities, this paper proposes a novel real-time distributed monitoring method with three levels, comprised of the terminal node, relay node, and server. Corresponding to these three monitoring levels, a vibration-based intelligent solution for recognizing the construction source is presented and compared with the traditional method. First, the blind source separation method was used to separate collected signals into a limited number of monitoring object sources; this helped to minimize the number of required classification categories and reduce the recognition uncertainty caused by signal mixing. Then, the mutual information (MI) method was used to select suitable vibration features, which were used as the input matrix for the resulting intelligent recognition. Finally, the construction behaviors were identified at the server based on returned features. Guided by this method, a sample dataset including pile-driving, train-operation, and environment-vibration signals was constructed and combined with a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The effects of blind source separation and the MI method are discussed in depth in this paper.
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17
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Ren B, Yang K, Zhu L, Hu L, Qiu T, Kong W, Zhang J. Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke. Front Comput Neurosci 2022; 16:785397. [PMID: 35431850 PMCID: PMC9008254 DOI: 10.3389/fncom.2022.785397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.
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Affiliation(s)
- Bin Ren
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Kun Yang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Li Zhu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Lang Hu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Tao Qiu
- Department of Neurology, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, China
| | - Wanzeng Kong
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Jianhai Zhang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
- *Correspondence: Jianhai Zhang
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18
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Yin Q, Shen D, Tang Y, Ding Q. Intelligent monitoring of noxious stimulation during anaesthesia based on heart rate variability analysis. Comput Biol Med 2022; 145:105408. [PMID: 35344869 DOI: 10.1016/j.compbiomed.2022.105408] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/13/2022] [Accepted: 03/12/2022] [Indexed: 01/03/2023]
Abstract
Research based on medical signals has received significant attention in recent years. If the patients' states can be accurately monitored based on medical signals, it greatly benefits both doctors and patients. This paper proposes a method to extract signal features from heart rate variability signals and classify patients' states using the long short-term memory network and enable effective monitoring of noxious stimulation. For data processing, the heart rate variability signal is decomposed and recombined by the empirical mode decomposition method, and the signal features of the noxious stimulation are extracted by the sliding time window method. Compared with the average accuracy of direct classifications, the classification accuracy based on the proposed method is proved more accurate. The model based on the extracted features proposed can realize the classification of consciousness and general anaesthesia with an accuracy rate of more than 90% and accurately estimate the occurrence of tracheal intubation stimulation. Furthermore, this study shows that combining the deep learning neural network with the extracted more effective signal features under different states and stresses can classify the states with high accuracy. Therefore, it is promising to apply the deep learning method in researching the autonomic nervous system.
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Affiliation(s)
- Qiang Yin
- Department of Mechanics, Tianjin University, Tianjin, 300350, China
| | - Dai Shen
- Department of Anesthesiology, Stomatology Hospital of Tianjin Medical University, Tianjin, 300070, China
| | - Ye Tang
- Department of Mechanics, Tianjin University, Tianjin, 300350, China
| | - Qian Ding
- Department of Mechanics, Tianjin University, Tianjin, 300350, China.
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19
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Cao J, Zhao Y, Shan X, Wei H, Guo Y, Chen L, Erkoyuncu JA, Sarrigiannis PG. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum Brain Mapp 2022; 43:860-879. [PMID: 34668603 PMCID: PMC8720201 DOI: 10.1002/hbm.25683] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Xiaocai Shan
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
- Institute of Geology and Geophysics, Chinese Academy of SciencesBeijingChina
| | - Hua‐liang Wei
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | - Yuzhu Guo
- School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
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20
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Analysis of electroencephalography (EEG) signals and adapting to systems theory principles. JOURNAL OF COMPLEXITY IN HEALTH SCIENCES 2021. [DOI: 10.21595/chs.2021.22038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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21
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Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia. Neural Plast 2021; 2021:6675052. [PMID: 34194488 PMCID: PMC8203359 DOI: 10.1155/2021/6675052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 11/17/2022] Open
Abstract
In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.
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22
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Shan X, Huo S, Yang L, Cao J, Zou J, Chen L, Sarrigiannis PG, Zhao Y. A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals. IEEE Trans Neural Syst Rehabil Eng 2021; 29:841-851. [PMID: 33909567 DOI: 10.1109/tnsre.2021.3076311] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.
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23
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Cao J, Grajcar K, Shan X, Zhao Y, Zou J, Chen L, Li Z, Grunewald R, Zis P, De Marco M, Unwin Z, Blackburn D, Sarrigiannis PG. Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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24
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Luo Z, Jin R, Shi H, Lu X. Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network. Neural Plast 2021; 2021:6655430. [PMID: 33628220 PMCID: PMC7895585 DOI: 10.1155/2021/6655430] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/07/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.
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Affiliation(s)
- Zhizeng Luo
- Institute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, China
| | - Ronghang Jin
- Institute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, China
| | - Hongfei Shi
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianju Lu
- Institute of Intelligent Control and Robotics, Hangzhou Dizanzi University, Hangzhou, China
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25
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A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test. Cogn Neurodyn 2020; 15:621-636. [PMID: 34367364 DOI: 10.1007/s11571-020-09646-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 09/07/2020] [Accepted: 10/21/2020] [Indexed: 10/23/2022] Open
Abstract
Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.
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26
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Huang W, Yan H, Wang C, Li J, Yang X, Li L, Zuo Z, Zhang J, Chen H. Long short-term memory-based neural decoding of object categories evoked by natural images. Hum Brain Mapp 2020; 41:4442-4453. [PMID: 32648632 PMCID: PMC7502843 DOI: 10.1002/hbm.25136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/19/2020] [Accepted: 06/29/2020] [Indexed: 01/18/2023] Open
Abstract
Visual perceptual decoding is one of the important and challenging topics in cognitive neuroscience. Building a mapping model between visual response signals and visual contents is the key point of decoding. Most previous studies used peak response signals to decode object categories. However, brain activities measured by functional magnetic resonance imaging are a dynamic process with time dependence, so peak signals cannot fully represent the whole process, which may affect the performance of decoding. Here, we propose a decoding model based on long short‐term memory (LSTM) network to decode five object categories from multitime response signals evoked by natural images. Experimental results show that the average decoding accuracy using the multitime (2–6 s) response signals is 0.540 from the five subjects, which is significantly higher than that using the peak ones (6 s; accuracy: 0.492; p < .05). In addition, from the perspective of different durations, methods and visual areas, the decoding performances of the five object categories are deeply and comprehensively explored. The analysis of different durations and decoding methods reveals that the LSTM‐based decoding model with sequence simulation ability can fit the time dependence of the multitime visual response signals to achieve higher decoding performance. The comparative analysis of different visual areas demonstrates that the higher visual cortex (VC) contains more semantic category information needed for visual perceptual decoding than lower VC.
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Affiliation(s)
- Wei Huang
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Hongmei Yan
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Chong Wang
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Jiyi Li
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Xiaoqing Yang
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Liang Li
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jiang Zhang
- Department of Medical Information Engineering, Sichuan University, Chengdu, China
| | - Huafu Chen
- The MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
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27
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Mora-Sánchez A, Pulini AA, Gaume A, Dreyfus G, Vialatte FB. A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments. Cogn Neurodyn 2020; 14:301-321. [PMID: 32399073 PMCID: PMC7203264 DOI: 10.1007/s11571-020-09573-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 01/31/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.
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Affiliation(s)
- Aldo Mora-Sánchez
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Alfredo-Aram Pulini
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Antoine Gaume
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Gérard Dreyfus
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - François-Benoît Vialatte
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
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28
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Tozzi A, Ahmad MZ, Peters JF. Neural computing in four spatial dimensions. Cogn Neurodyn 2020; 15:349-357. [PMID: 33854648 DOI: 10.1007/s11571-020-09598-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 04/26/2020] [Accepted: 05/11/2020] [Indexed: 12/22/2022] Open
Abstract
Relationships among near set theory, shape maps and recent accounts of the Quantum Hall effect pave the way to neural networks computations performed in higher dimensions. We illustrate the operational procedure to build a real or artificial neural network able to detect, assess and quantify a fourth spatial dimension. We show how, starting from two-dimensional shapes embedded in a 2D topological charge pump, it is feasible to achieve the corresponding four-dimensional shapes, which encompass a larger amount of information. Synthesis of surface shape components, viewed topologically as shape descriptions in the form of feature vectors that vary over time, leads to a 4D view of cerebral activity. This novel, relatively straightforward architecture permits to increase the amount of available qbits in a fixed volume.
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Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - Muhammad Zubair Ahmad
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada
| | - James F Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada
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Pan H, Mi W, Wen F, Zhong W. An adaptive decoder design based on the receding horizon optimization in BMI system. Cogn Neurodyn 2020; 14:281-290. [PMID: 32399071 DOI: 10.1007/s11571-019-09567-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 12/10/2019] [Accepted: 12/13/2019] [Indexed: 10/25/2022] Open
Abstract
In a motor brain-machine interface system, since the electroencephalogram signal is changing through out the process of the arm movement, the offline trained decoder with fixed weights is often unable to convert the electroencephalogram signal accurately, resulting in poor recovery of joint motor function. In this paper, a receding horizon optimization strategy is chosen to online update the decoder weights and design an adaptive Wiener-filter-based decoder. Firstly, a classical Wiener-filter-based decoder with fixed weights is brief reviewed. Secondly, the weights in Wiener-filter-based decoder are updated by minimizing the cost function, which is composed by the sum of squared position errors in the given horizon at each sampling time. The simulation shows that the recovery effect of joint motor function and neuron activity in the BMI system with the adaptive decoder are both better than that in the BMI system with the fixed decoder.
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Affiliation(s)
- Hongguang Pan
- 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China
| | - Wenyu Mi
- 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China
| | - Fan Wen
- 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 China
| | - Weimin Zhong
- 2Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
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Čukić M, Stokić M, Simić S, Pokrajac D. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn 2020; 14:443-455. [PMID: 32655709 DOI: 10.1007/s11571-020-09581-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 02/18/2020] [Accepted: 03/06/2020] [Indexed: 01/05/2023] Open
Abstract
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.
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Affiliation(s)
- Milena Čukić
- Department for General Physiology and Biophysics, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, 11 000 Serbia
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Madrid, Spain
| | - Miodrag Stokić
- Life Activities Advancement Center, Gospodar Jovanova 35, Belgrade, 11 000 Serbia
- Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Slobodan Simić
- Institute for Mental Health, Palmotićeva 37, Belgrade, Serbia
| | - Dragoljub Pokrajac
- Delaware Biotechnology Institute, Delaware State University, 305D Science Center North, 1200 N Dupont Hwy, Dover, DE 19901 USA
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