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Eqlimi E, Bockstael A, Schönwiesner M, Talsma D, Botteldooren D. Time course of EEG complexity reflects attentional engagement during listening to speech in noise. Eur J Neurosci 2023; 58:4043-4069. [PMID: 37814423 DOI: 10.1111/ejn.16159] [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: 03/02/2023] [Revised: 08/31/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.
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Affiliation(s)
- Ehsan Eqlimi
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Annelies Bockstael
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | | | - Durk Talsma
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Dick Botteldooren
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
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Zhong L, Wan J, Wu J, He S, Zhong X, Huang Z, Li Z. Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy. Front Neuroinform 2022; 16:962466. [PMID: 36059863 PMCID: PMC9433125 DOI: 10.3389/fninf.2022.962466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Objective During the transition from normal to seizure and then to termination, electroencephalography (EEG) signals have complex changes in time-frequency-spatial characteristics. The quantitative analysis of EEG characteristics and the exploration of their dynamic propagation in this paper would help to provide new biomarkers for distinguishing between pre-ictal and inter-ictal states and to better understand the seizure mechanisms. Methods Thirty-three children with absence epilepsy were investigated with EEG signals. Power spectral and synchronization were combined to provide the time-frequency-spatial characteristics of EEG and analyze the spatial distribution and propagation of EEG in the brain with topographic maps. To understand the mechanism of spatial-temporal evolution, we compared inter-ictal, pre-ictal, and ictal states in EEG power spectral and synchronization network and its rhythms in each frequency band. Results Power, frequency, and spatial synchronization are all enhanced during the absence seizures to jointly dominate the epilepsy process. We confirmed that a rapid diffusion at the onset accompanied by the frontal region predominance exists. The EEG power rapidly bursts in 2–4 Hz through the whole brain within a few seconds after the onset. This spatiotemporal evolution is associated with spatial diffusion and brain regions interaction, with a similar pattern, increasing first and then decreasing, in both the diffusion of the EEG power and the connectivity of the brain network during the childhood absence epilepsy (CAE) seizures. Compared with the inter-ictal group, we observed increases in power of delta and theta rhythms in the pre-ictal group (P < 0.05). Meanwhile, the synchronization of delta rhythm decreased while that of alpha rhythm enhanced. Conclusion The initiation and propagation of CAE seizures are related to the abnormal discharge diffusion and the synchronization network. During the seizures, brain activity is completely changed with the main component delta rhythm. Furthermore, this article demonstrated for the first time that alpha inhibition, which is consistent with the brain’s feedback regulation mechanism, is caused by the enhancement of the network connection. Temporal and spatial evolution of EEG is of great significance for the transmission mechanism, clinical diagnosis and automatic detection of absence epilepsy seizures.
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Affiliation(s)
- Lisha Zhong
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jiangzhong Wan
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jia Wu
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Suling He
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xuefei Zhong
- Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Zhiwei Huang
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
| | - Zhangyong Li
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- *Correspondence: Zhangyong Li,
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Benkő Z, Bábel T, Somogyvári Z. Model-free detection of unique events in time series. Sci Rep 2022; 12:227. [PMID: 34996940 PMCID: PMC8742065 DOI: 10.1038/s41598-021-03526-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/29/2021] [Indexed: 11/10/2022] Open
Abstract
Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years.
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Affiliation(s)
- Zsigmond Benkő
- Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, 1121, Hungary.,János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Ullői road 26, Budapest, 1085, Hungary
| | - Tamás Bábel
- Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, 1121, Hungary
| | - Zoltán Somogyvári
- Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, 1121, Hungary.
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Gao Z, Lu G, Yan P, Lyu C, Li X, Shang W, Xie Z, Zhang W. Automatic Change Detection for Real-Time Monitoring of EEG Signals. Front Physiol 2018; 9:325. [PMID: 29670541 PMCID: PMC5893758 DOI: 10.3389/fphys.2018.00325] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 03/15/2018] [Indexed: 11/19/2022] Open
Abstract
In recent years, automatic change detection for real-time monitoring of electroencephalogram (EEG) signals has attracted widespread interest with a large number of clinical applications. However, it is still a challenging problem. This paper presents a novel framework for this task where joint time-domain features are firstly computed to extract temporal fluctuations of a given EEG data stream; and then, an auto-regressive (AR) linear model is adopted to model the data and temporal anomalies are subsequently calculated from that model to reflect the possibilities that a change occurs; a non-parametric statistical test based on Randomized Power Martingale (RPM) is last performed for making change decision from the resulting anomaly scores. We conducted experiments on the publicly-available Bern-Barcelona EEG database where promising results for terms of detection precision (96.97%), detection recall (97.66%) as well as computational efficiency have been achieved. Meanwhile, we also evaluated the proposed method for real detection of seizures occurrence for a monitoring epilepsy patient. The results of experiments by using both the testing database and real application demonstrated the effectiveness and feasibility of the method for the purpose of change detection in EEG signals. The proposed framework has two additional properties: (1) it uses a pre-defined AR model for modeling of the past observed data so that it can be operated in an unsupervised manner, and (2) it uses an adjustable threshold to achieve a scalable decision making so that a coarse-to-fine detection strategy can be developed for quick detection or further analysis purposes.
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Affiliation(s)
- Zhen Gao
- Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China
| | - Guoliang Lu
- Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China
| | - Peng Yan
- Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China
| | - Chen Lyu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Xueyong Li
- Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China
| | - Wei Shang
- Institute of Neurology, Shandong University, Jinan, China.,Department of Neurology, Second Hospital of Shandong University, Jinan, China
| | - Zhaohong Xie
- Institute of Neurology, Shandong University, Jinan, China.,Department of Neurology, Second Hospital of Shandong University, Jinan, China
| | - Wanming Zhang
- Medical Imaging Center, Second Hospital of Shandong University, Jinan, China
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Zorick T, Smith J. Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:6450126. [PMID: 27516806 PMCID: PMC4969566 DOI: 10.1155/2016/6450126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/28/2016] [Accepted: 06/19/2016] [Indexed: 11/18/2022]
Abstract
Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014), the concept of information equilibrium (IE) in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates) information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally.
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Affiliation(s)
- Todd Zorick
- Department of Psychiatry, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA; Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, USA
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Weng WC, Jiang GJA, Chang CF, Lu WY, Lin CY, Lee WT, Shieh JS. Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy. PLoS One 2015; 10:e0134083. [PMID: 26244497 PMCID: PMC4526647 DOI: 10.1371/journal.pone.0134083] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 07/06/2015] [Indexed: 12/02/2022] Open
Abstract
Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.
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Affiliation(s)
- Wen-Chin Weng
- Department of Life Science, National Taiwan University, Taipei, Taiwan
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Pediatrics, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pediatric Neurology, National Taiwan University Children’s Hospital, Taipei, Taiwan
| | - George J. A. Jiang
- Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, Taiwan
| | - Chi-Feng Chang
- Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, Taiwan
| | - Wen-Yu Lu
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Chun-Yen Lin
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Wang-Tso Lee
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Pediatrics, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pediatric Neurology, National Taiwan University Children’s Hospital, Taipei, Taiwan
- Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
- * E-mail:
| | - Jiann-Shing Shieh
- Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Chung-Li, Taiwan
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li, Taiwan
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li, Taiwan
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Gao J, Hu J, Liu F, Cao Y. Multiscale entropy analysis of biological signals: a fundamental bi-scaling law. Front Comput Neurosci 2015; 9:64. [PMID: 26082711 PMCID: PMC4451367 DOI: 10.3389/fncom.2015.00064] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2014] [Accepted: 05/14/2015] [Indexed: 11/13/2022] Open
Abstract
Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG.
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Affiliation(s)
- Jianbo Gao
- Institute of Complexity Science and Big Data Technology, Guangxi University Nanning, China ; PMB Intelligence LLC Sunnyvale, CA, USA
| | - Jing Hu
- PMB Intelligence LLC Sunnyvale, CA, USA
| | - Feiyan Liu
- Institute of Complexity Science and Big Data Technology, Guangxi University Nanning, China ; School of Management, University of Chinese Academy of Sciences Beijing, China
| | - Yinhe Cao
- Institute of Complexity Science and Big Data Technology, Guangxi University Nanning, China ; PMB Intelligence LLC Sunnyvale, CA, USA
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