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Liu L, Zheng R, Wu D, Yuan Y, Lin Y, Wang D, Jiang T, Cao J, Xu Y. Global and multi-partition local network analysis of scalp EEG in West syndrome before and after treatment. Neural Netw 2024; 179:106540. [PMID: 39079377 DOI: 10.1016/j.neunet.2024.106540] [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: 01/10/2024] [Revised: 04/12/2024] [Accepted: 07/12/2024] [Indexed: 09/18/2024]
Abstract
West syndrome is an epileptic disease that seriously affects the normal growth and development of infants in early childhood. Based on the methods of brain topological network and graph theory, this article focuses on three clinical states of patients before and after treatment. In addition to discussing bidirectional and unidirectional global networks from the perspective of computational principles, a more in-depth analysis of local intra-network and inter-network characteristics of multi-partitioned networks is also performed. The spatial feature distribution based on feature path length is introduced for the first time. The results show that the bidirectional network has better significant differentiation. The rhythmic feature change trend and spatial characteristic distribution of this network can be used as a measure of the impact on global information processing in the brain after treatment. And localized brain regions variability in features and differences in the ability to interact with information between brain regions have potential as biomarkers for medication assessment in WEST syndrome. The above shows specific conclusions on the interaction relationship and consistency of macro-network and micro-network, which may have a positive effect on patients' treatment and prognosis management.
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Affiliation(s)
- Lishan Liu
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310052, China.
| | - Runze Zheng
- Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou, 310018, China; Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Duanpo Wu
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310052, China; Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou, 310018, China.
| | - Yixuan Yuan
- Department of Electronic Engineering, The Chinese University of Hong Kong, 999077, Hong Kong, China.
| | - Yi Lin
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310052, China.
| | - Danping Wang
- Plateforme d'Etude de la Sensorimotricité (PES), BioMedTech Facilities, Université Paris Cité, Paris, 75270, France.
| | - Tiejia Jiang
- Children's Hospital, Zhejiang University School of Medicine, Hangzhou, 310018, China.
| | - Jiuwen Cao
- Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou, 310018, China; Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, 310018, China; Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, 311100, China.
| | - Yuansheng Xu
- Department of Emergency, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, China.
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2
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Stampacchia S, Asadi S, Tomczyk S, Ribaldi F, Scheffler M, Lövblad KO, Pievani M, Fall AB, Preti MG, Unschuld PG, Van De Ville D, Blanke O, Frisoni GB, Garibotto V, Amico E. Fingerprints of brain disease: connectome identifiability in Alzheimer's disease. Commun Biol 2024; 7:1169. [PMID: 39294332 PMCID: PMC11411139 DOI: 10.1038/s42003-024-06829-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024] Open
Abstract
Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.
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Affiliation(s)
- Sara Stampacchia
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Saina Asadi
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Karl-Olof Lövblad
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Neurodiagnostic and Neurointerventional Division, Geneva University Hospitals, Geneva, Switzerland
| | - Michela Pievani
- Lab of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Aïda B Fall
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Paul G Unschuld
- Division of Geriatric Psychiatry, University Hospitals of Geneva (HUG), 1226, Thônex, Switzerland
- Department of Psychiatry, University of Geneva (UniGE), 1205, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Olaf Blanke
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- School of Mathematics, University of Birmingham, Birmingham, UK.
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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3
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Dimitriadis SI. ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns. Comput Biol Med 2024; 180:108862. [PMID: 39068901 DOI: 10.1016/j.compbiomed.2024.108862] [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: 02/11/2024] [Revised: 06/30/2024] [Accepted: 07/06/2024] [Indexed: 07/30/2024]
Abstract
Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Passeig Vall D'Hebron 171, 08035, Barcelona, Spain; Institut de Neurociencies, University of Barcelona, Municipality of Horta-Guinardó, 08035, Barcelona, Spain; Integrative Neuroimaging Lab, Thessaloniki, 55133, Makedonia, Greece; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Rd, CF24 4HQ, Cardiff, Wales, United Kingdom.
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4
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N P GS, Singh BK. Analysis of reading-task-based brain connectivity in dyslexic children using EEG signals. Med Biol Eng Comput 2024; 62:2355-2369. [PMID: 38584207 DOI: 10.1007/s11517-024-03085-0] [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: 08/16/2023] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
Developmental dyslexia, a neurodevelopment reading disorder, can impact even children with average intelligence. The present study examined the brain connectivity in dyslexic and control children during the reading task using graph theory. 19-channel electroencephalogram (EEG) signals were recorded from 15 dyslexic children and 15 control children. Functional connectivity was estimated by measuring the EEG coherence at 19 electrode locations, and graph measures were calculated using the graph theory method. Reading task results identified deprived task performance in dyslexic children against controls. Graph measures revealed longer path length, reduced clustering coefficient and reduced network efficiencies (in theta and alpha bands) of dyslexic group. At the nodal level, we found a significant increase in delta strength (T4 and T5 electrode locations) and reduced strength in theta (T6, P4, Fp1, F8 and F3) and alpha bands (T4, T3, P4 and F3) during the reading task in dyslexic group. In conclusion, the present study identified distinct graph measures between groups when performing a reading task and showed possible evidence for compromised brain networks in dyslexic group.
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Affiliation(s)
- Guhan Seshadri N P
- Department of Biomedical Engineering, National Institute of Technology Raipur, G.E Road, Raipur, 492010, India
| | - Bikesh Kumar Singh
- Department of Biomedical Engineering, National Institute of Technology Raipur, G.E Road, Raipur, 492010, India.
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5
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Nagy P, Tóth B, Winkler I, Boncz Á. The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks. Hum Brain Mapp 2024; 45:e26747. [PMID: 38825981 PMCID: PMC11144954 DOI: 10.1002/hbm.26747] [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: 07/04/2023] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
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Affiliation(s)
- Péter Nagy
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Faculty of Electrical Engineering and Informatics, Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
| | - Brigitta Tóth
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - István Winkler
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Ádám Boncz
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
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6
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Lin PJ, Li W, Zhai X, Sun J, Pan Y, Ji L, Li C. AM-EEGNet: An advanced multi-input deep learning framework for classifying stroke patient EEG task states. Neurocomputing 2024; 585:127622. [DOI: 10.1016/j.neucom.2024.127622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2024]
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7
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [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: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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8
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Seshadri NPG, Singh BK, Pachori RB. EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4672-4682. [PMID: 37988207 DOI: 10.1109/tnsre.2023.3335806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.
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Li M, Yang J, Tian W, Ju X. Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network. Front Comput Neurosci 2023; 17:1263710. [PMID: 38024448 PMCID: PMC10644253 DOI: 10.3389/fncom.2023.1263710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed.
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10
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Kampel N, Kiefer CM, Shah NJ, Neuner I, Dammers J. Neural fingerprinting on MEG time series using MiniRocket. Front Neurosci 2023; 17:1229371. [PMID: 37799343 PMCID: PMC10547883 DOI: 10.3389/fnins.2023.1229371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.
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Affiliation(s)
- Nikolas Kampel
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
| | - Christian M. Kiefer
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-11), Jülich Aachen Research Alliance (JARA), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – BRAIN – Translational Medicine, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Jülich Aachen Research Alliance (JARA) – CSD – Center for Simulation and Data Science, Aachen, Germany
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11
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Xu J, Zhou E, Qin Z, Bi T, Qin Z. Electroencephalogram-Based Subject Matching Learning (ESML): A Deep Learning Framework on Electroencephalogram-Based Biometrics and Task Identification. Behav Sci (Basel) 2023; 13:765. [PMID: 37754043 PMCID: PMC10525823 DOI: 10.3390/bs13090765] [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: 05/31/2023] [Revised: 07/20/2023] [Accepted: 08/07/2023] [Indexed: 09/28/2023] Open
Abstract
An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user identification and tack classification. ESML consists of two parts: one is the ESML1 model via an LSTM-based method for EEG-user linking, and one is the ESML2 model via a CNN-based method for EEG-task linking. The new model ESML is simple, but effective and efficient. It does not require any restrictions for EEG data collection on motions and thinking for users, and it does not need any EEG preprocessing operations, such as EEG denoising and feature extraction. The experiments were conducted on three public datasets and the results show that ESML performs the best and achieves significant performance improvement when compared to baseline methods (i.e., SVM, LDA, NN, DTS, Bayesian, AdaBoost and MLP). The ESML1 model provided the best precision at 96% with 109 users and the ESML2 model achieved 99% precision at 3-Class task classification. These experimental results provide direct evidence that EEG signals can be used for user identification and task classification.
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Affiliation(s)
- Jin Xu
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610097, China; (J.X.); (Z.Q.); (Z.Q.)
| | - Erqiang Zhou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610097, China; (J.X.); (Z.Q.); (Z.Q.)
| | - Zhen Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610097, China; (J.X.); (Z.Q.); (Z.Q.)
| | - Ting Bi
- Department of Computer Science, Maynooth University, W23 F2K8 Maynooth, Ireland
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610097, China; (J.X.); (Z.Q.); (Z.Q.)
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12
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TajDini M, Sokolov V, Kuzminykh I, Ghita B. Brainwave-based authentication using features fusion. Comput Secur 2023. [DOI: 10.1016/j.cose.2023.103198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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13
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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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14
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Ortega-Rodríguez J, Gómez-González JF, Pereda E. Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094239. [PMID: 37177443 PMCID: PMC10181121 DOI: 10.3390/s23094239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.
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Affiliation(s)
- Jordan Ortega-Rodríguez
- Department of Industrial Engineering, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain
- IACTEC Medical Technology Group, Instituto de Astrofísica de Canarias (IAC), 38320 San Cristóbal de La Laguna, Spain
| | | | - Ernesto Pereda
- Department of Industrial Engineering, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain
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15
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Wan Z, Cheng W, Li M, Zhu R, Duan W. GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition. Front Neurosci 2023; 17:1160040. [PMID: 37123356 PMCID: PMC10133471 DOI: 10.3389/fnins.2023.1160040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Background Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition. Method Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model. Results Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%. Conclusion Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.
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Affiliation(s)
- Zhijiang Wan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China
| | - Wangxinjun Cheng
- Queen Mary College of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
| | - Manyu Li
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
| | - Renping Zhu
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China
- Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China
- School of Information Management, Wuhan University, Wuhan, China
| | - Wenfeng Duan
- The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China
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16
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Oikonomou VP. Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:2425. [PMID: 36904629 PMCID: PMC10006983 DOI: 10.3390/s23052425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.
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Affiliation(s)
- Vangelis P Oikonomou
- Information Technologies Institute, Centre for Research and Technology Hellas, Thermi-Thessaloniki, 57001 Thessaloniki, Greece
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17
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Maria Pani S, Saba L, Fraschini M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin Neurophysiol 2022; 143:1-13. [DOI: 10.1016/j.clinph.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/03/2022]
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18
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Ashenaei R, Asghar Beheshti A, Yousefi Rezaii T. Stable EEG-Based biometric system using functional connectivity based on Time-Frequency features with optimal channels. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Jäncke L, Valizadeh SA. Identification of individual subjects based on neuroanatomical measures obtained seven years earlier. Eur J Neurosci 2022; 56:4642-4652. [PMID: 35831945 PMCID: PMC9543309 DOI: 10.1111/ejn.15770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/07/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022]
Abstract
We analyzed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural MRI over the course of seven years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume (ICV), total cortical thickness (CT), total cortical surface area (CA), cortical gray matter (CoGM), cerebral white matter (CeWM), cerebellar cortex (CBGM), cerebellar white matter (CBWM), subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala, accumbens], and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF), and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some 4-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques.
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Affiliation(s)
- L Jäncke
- Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program "Dynamics of Healthy Aging," University Zurich
| | - S A Valizadeh
- Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
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20
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Wang M, Yin X, Zhu Y, Hu J. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey. SENSORS (BASEL, SWITZERLAND) 2022; 22:5111. [PMID: 35890799 PMCID: PMC9320620 DOI: 10.3390/s22145111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 01/27/2023]
Abstract
Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.
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Affiliation(s)
- Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
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21
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Mean curve length: An efficient feature for brainwave biometrics. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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22
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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: 57] [Impact Index Per Article: 28.5] [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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23
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Zeng H, Jin Y, Wu Q, Pan D, Xu F, Zhao Y, Hu H, Kong W. EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation. Front Psychiatry 2022; 13:928781. [PMID: 35898631 PMCID: PMC9309393 DOI: 10.3389/fpsyt.2022.928781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.
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Affiliation(s)
- Hong Zeng
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China.,Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China
| | - Yanping Jin
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Qi Wu
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Deng Pan
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Feifan Xu
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Yue Zhao
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Hua Hu
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China
| | - Wanzeng Kong
- College of Computer and Technology, Hangzhou Dianzi University, Hangzhou, China.,Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China
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24
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Zhou Y, Hu L, Yu T, Li Y. A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention. Front Neurosci 2022; 15:746146. [PMID: 34970111 PMCID: PMC8712654 DOI: 10.3389/fnins.2021.746146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 12/04/2022] Open
Abstract
Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.
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Affiliation(s)
- Yajun Zhou
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Li Hu
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Tianyou Yu
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
| | - Yuanqing Li
- Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.,Guangzhou Key Laboratory of Brain Computer Interaction and Application, Guangzhou, China
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25
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CNN-Based Personal Identification System Using Resting State Electroencephalography. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1160454. [PMID: 34938327 PMCID: PMC8687816 DOI: 10.1155/2021/1160454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/16/2021] [Indexed: 11/18/2022]
Abstract
As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.
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26
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Wu CT, Huang HC, Huang S, Chen IM, Liao SC, Chen CK, Lin C, Lee SH, Chen MH, Tsai CF, Weng CH, Ko LW, Jung TP, Liu YH. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset. BIOSENSORS 2021; 11:499. [PMID: 34940256 PMCID: PMC8699348 DOI: 10.3390/bios11120499] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/26/2021] [Accepted: 12/04/2021] [Indexed: 05/09/2023]
Abstract
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.
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Affiliation(s)
- Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo 113-0033, Japan;
| | - Hao-Chuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Shiuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - I-Ming Chen
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
- Institute of Health Policy and Management, National Taiwan University, Taipei 10617, Taiwan
| | - Shih-Cheng Liao
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
| | - Chih-Ken Chen
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Chemin Lin
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Shwu-Hua Lee
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chang-Hsin Weng
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Li-Wei Ko
- Department of Bio Science & Tech., National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Tzyy-Ping Jung
- Institute for Neural Computation, University of California, San Diego, CA 92093, USA
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
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27
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Di Y, An X, Zhong W, Liu S, Ming D. The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG. Front Hum Neurosci 2021; 15:672946. [PMID: 34588964 PMCID: PMC8475761 DOI: 10.3389/fnhum.2021.672946] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85-100% for intra-experiment dataset, and were 80-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.
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Affiliation(s)
- Yang Di
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xingwei An
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Wenxiao Zhong
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Shuang Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dong Ming
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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Brief segments of neurophysiological activity enable individual differentiation. Nat Commun 2021; 12:5713. [PMID: 34588439 PMCID: PMC8481307 DOI: 10.1038/s41467-021-25895-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
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29
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Fan J, Jiang X, Liu X, Zhao X, Ye X, Dai C, Akay M, Chen W. Cancelable HD-sEMG Biometric Identification Via Deep Feature Learning. IEEE J Biomed Health Inform 2021; 26:1782-1793. [PMID: 34582353 DOI: 10.1109/jbhi.2021.3115784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Conventional biometric modalities, such as face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancellable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancellable and could be customized by the users through performing finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects x 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.
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30
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Zhang Y, Li M, Shen H, Hu D. On the Specificity and Permanence of Electroencephalography Functional Connectivity. Brain Sci 2021; 11:1266. [PMID: 34679331 PMCID: PMC8722434 DOI: 10.3390/brainsci11101266] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/06/2021] [Accepted: 09/17/2021] [Indexed: 11/17/2022] Open
Abstract
Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications.
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Affiliation(s)
| | - Ming Li
- College of Intelligence Science and Technology, National
University of Defense Technology, Changsha 410073, China;
(Y.Z.); (H.S.);
(D.H.)
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31
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Kang JH, Youn J, Kim SH, Kim J. Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance. Front Neurosci 2021; 15:663101. [PMID: 34483816 PMCID: PMC8414888 DOI: 10.3389/fnins.2021.663101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/27/2021] [Indexed: 12/01/2022] Open
Abstract
Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain–computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.
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Affiliation(s)
- Jae-Hwan Kang
- AI Grand ICT Research Center, Dong-eui University, Busan, South Korea
| | - Joosang Youn
- Department of Industrial ICT Engineering, Dong-eui University, Busan, South Korea
| | - Sung-Hee Kim
- Department of Industrial ICT Engineering, Dong-eui University, Busan, South Korea
| | - Junsuk Kim
- Department of Industrial ICT Engineering, Dong-eui University, Busan, South Korea
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32
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Panzino A, Orrù G, Marcialis GL, Roli F. EEG personal recognition based on ‘qualified majority’ over signal patches. IET BIOMETRICS 2021. [DOI: 10.1049/bme2.12050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Andrea Panzino
- Department of Electrical and Electronic Engineering (DIEE) University of Cagliari Cagliari Italy
| | - Giulia Orrù
- Department of Electrical and Electronic Engineering (DIEE) University of Cagliari Cagliari Italy
| | - Gian Luca Marcialis
- Department of Electrical and Electronic Engineering (DIEE) University of Cagliari Cagliari Italy
| | - Fabio Roli
- Department of Electrical and Electronic Engineering (DIEE) University of Cagliari Cagliari Italy
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33
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Sousouri G, Krugliakova E, Skorucak J, Leach S, Snipes S, Ferster ML, Da Poian G, Karlen W, Huber R. Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep. Sleep 2021; 45:6347149. [PMID: 34373925 DOI: 10.1093/sleep/zsab204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/15/2021] [Indexed: 11/12/2022] Open
Abstract
The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.
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Affiliation(s)
- Georgia Sousouri
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Elena Krugliakova
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Jelena Skorucak
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Sven Leach
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Sophia Snipes
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Maria Laura Ferster
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Giulia Da Poian
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Walter Karlen
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Reto Huber
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zürich, Zurich, Switzerland
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34
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Orkan Olcay B, Özgören M, Karaçalı B. On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels. Neural Netw 2021; 143:452-474. [PMID: 34273721 DOI: 10.1016/j.neunet.2021.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/04/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.
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Affiliation(s)
- B Orkan Olcay
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, Near East University, 99138, Nicosia, Cyprus.
| | - Bilge Karaçalı
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
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35
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Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
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Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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36
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Jijomon C, Vinod A. Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102739] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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37
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38
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Cattai T, Colonnese S, Corsi MC, Bassett DS, Scarano G, De Vico Fallani F. Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1168-1177. [PMID: 34115589 DOI: 10.1109/tnsre.2021.3088637] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and we investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and we compared the discriminant power with respect to standard power spectrum features. At the group level, results showed that while spectral-coherence based network features were increasing in the sensorimotor areas, those based on imaginary-coherence were significantly decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we eventually assessed the potential of these network connectivity features in a simple off-line classification scenario. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.
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39
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Urdanibia-Centelles O, Nielsen RM, Rostrup E, Vedel-Larsen E, Thomsen K, Nikolic M, Johnsen B, Møller K, Lauritzen M, Benedek K. Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis. Clin Neurophysiol 2021; 132:2075-2082. [PMID: 34284242 DOI: 10.1016/j.clinph.2021.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/12/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients. METHODS We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed. RESULTS Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). CONCLUSIONS Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers. SIGNIFICANCE Automatic cEEG analysis provides useful information in septic patients.
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Affiliation(s)
- Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Rikke M Nielsen
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Esben Vedel-Larsen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Kirsten Thomsen
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Miki Nikolic
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark.
| | - Kirsten Møller
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Denmark.
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
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40
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Jiang X, Xu K, Liu X, Dai C, Clifton DA, Clancy EA, Akay M, Chen W. Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification. IEEE J Biomed Health Inform 2021; 25:1070-1079. [PMID: 32991293 DOI: 10.1109/jbhi.2020.3027389] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.
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41
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Boubakeur MR, Wang G. Self-Relative Evaluation Framework for EEG-Based Biometric Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:2097. [PMID: 33802708 PMCID: PMC8002517 DOI: 10.3390/s21062097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/14/2021] [Accepted: 03/15/2021] [Indexed: 11/16/2022]
Abstract
In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.
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Affiliation(s)
- Meriem Romaissa Boubakeur
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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42
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Kottlarz I, Berg S, Toscano-Tejeida D, Steinmann I, Bähr M, Luther S, Wilke M, Parlitz U, Schlemmer A. Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities. Front Physiol 2021; 11:614565. [PMID: 33597891 PMCID: PMC7882607 DOI: 10.3389/fphys.2020.614565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/16/2020] [Indexed: 11/30/2022] Open
Abstract
In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.
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Affiliation(s)
- Inga Kottlarz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Sebastian Berg
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Diana Toscano-Tejeida
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Iris Steinmann
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Stefan Luther
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Melanie Wilke
- Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany.,German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Ulrich Parlitz
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Alexander Schlemmer
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
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43
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Shirzhiyan Z, Keihani A, Farahi M, Shamsi E, GolMohammadi M, Mahnam A, Haidari MR, Jafari AH. Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI. Front Neurosci 2020; 14:534619. [PMID: 33328841 PMCID: PMC7718037 DOI: 10.3389/fnins.2020.534619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.
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Affiliation(s)
- Zahra Shirzhiyan
- Computational Neuroscience, Institute of Medical Technology, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.,Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmadreza Keihani
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Morteza Farahi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Shamsi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina GolMohammadi
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Amin Mahnam
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohsen Reza Haidari
- Section of Neuroscience, Department of Neurology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
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44
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Fraschini M, Meli M, Demuru M, Didaci L, Barberini L. EEG Fingerprints under Naturalistic Viewing Using a Portable Device. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6565. [PMID: 33212929 PMCID: PMC7698321 DOI: 10.3390/s20226565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 11/13/2020] [Accepted: 11/16/2020] [Indexed: 06/11/2023]
Abstract
The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.
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Affiliation(s)
- Matteo Fraschini
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Miro Meli
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Matteo Demuru
- Stichting Epilepsie Instellingen Nederland (SEIN), 2103SW Heemstede, The Netherlands;
| | - Luca Didaci
- Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy; (M.M.); (L.D.)
| | - Luigi Barberini
- Department of Medical Sciences and Public Health, University of Cagliari, 09123 Cagliari, Italy;
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45
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Mohamed AMA, Uçan ON, Bayat O, Duru AD. Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG). Appl Bionics Biomech 2020; 2020:8853238. [PMID: 33224269 PMCID: PMC7673949 DOI: 10.1155/2020/8853238] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 10/02/2020] [Accepted: 10/22/2020] [Indexed: 02/01/2023] Open
Abstract
An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.
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Affiliation(s)
- Ahmed M. A. Mohamed
- School of Engineering and Natural Sciences, Altinbas University, 34217, Turkey
- Department of Computer Science, The Libyan Academy, 16063 Benghazi, Libya
| | - Osman N. Uçan
- School of Engineering and Natural Sciences, Altinbas University, 34217, Turkey
| | - Oğuz Bayat
- School of Engineering and Natural Sciences, Altinbas University, 34217, Turkey
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46
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Jin X, Tang J, Kong X, Peng Y, Cao J, Zhao Q, Kong W. CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition. IEEE Trans Neural Syst Rehabil Eng 2020; 29:103-112. [PMID: 33147145 DOI: 10.1109/tnsre.2020.3035786] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks.
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47
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Monsy JC, Vinod AP. EEG‐based biometric identification using frequency‐weighted power feature. IET BIOMETRICS 2020. [DOI: 10.1049/iet-bmt.2019.0158] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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48
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Barayeu U, Horlava N, Libert A, Van Hulle M. Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier. BIOSENSORS-BASEL 2020; 10:bios10090124. [PMID: 32933146 PMCID: PMC7558120 DOI: 10.3390/bios10090124] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/07/2020] [Accepted: 09/11/2020] [Indexed: 11/16/2022]
Abstract
The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively.
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Affiliation(s)
- Uladzislau Barayeu
- Department of Biophysics, Belarusian State University, 220030 Minsk, Belarus;
| | - Nastassya Horlava
- Department of Mathematical Modelling and Data Analysis, Belarusian State University, 220030 Minsk, Belarus;
| | - Arno Libert
- Laboratory for Neuro- and Psychophysiology, Department of Neuroscience, KU Leuven, O&N2, Herestraat 49, 3000 Leuven, Belgium
- Correspondence: (A.L.); (M.V.H.)
| | - Marc Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neuroscience, KU Leuven, O&N2, Herestraat 49, 3000 Leuven, Belgium
- Correspondence: (A.L.); (M.V.H.)
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49
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Moctezuma LA, Molinas M. Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection. Sci Rep 2020; 10:14917. [PMID: 32913275 PMCID: PMC7484900 DOI: 10.1038/s41598-020-72051-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/20/2020] [Indexed: 11/13/2022] Open
Abstract
We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR). This method was tested on EEG signals from 109 subjects of the public motor movement/imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. We were able to obtain a TAR of \documentclass[12pt]{minimal}
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\begin{document}$$1.000 \pm 0.000$$\end{document}1.000±0.000 and TRR of \documentclass[12pt]{minimal}
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\begin{document}$$0.998 \pm 0.001$$\end{document}0.998±0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to \documentclass[12pt]{minimal}
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\begin{document}$$0.993 \pm 0.01$$\end{document}0.993±0.01 and a TRR of up to \documentclass[12pt]{minimal}
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\begin{document}$$0.941 \pm 0.002$$\end{document}0.941±0.002 for the Pareto-front, using NSGA-III and DWT-based features in the resting-state with the eyes-open. In the resting-state with the eyes-closed, the TAR was \documentclass[12pt]{minimal}
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\begin{document}$$0.997 \pm 0.02$$\end{document}0.997±0.02 and the TRR \documentclass[12pt]{minimal}
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\begin{document}$$0.950 \pm 0.05,$$\end{document}0.950±0.05, also using DWT-based features from three channels. These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. Furthermore, the candidates obtained throughout the optimization process of NSGA-III showed that it is possible to obtain TARs and TRRs above 0.900 using LOF and DWT- or EMD-based features with only one to three EEG channels, opening the way to testing this approach on bigger datasets to develop a more realistic and usable EEG-based biometric system.
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Affiliation(s)
- Luis Alfredo Moctezuma
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
| | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491, Trondheim, Norway
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50
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Wilaiprasitporn T, Ditthapron A, Matchaparn K, Tongbuasirilai T, Banluesombatkul N, Chuangsuwanich E. Affective EEG-Based Person Identification Using the Deep Learning Approach. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2019.2924648] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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