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Dirik HB, Ertan H. Hemispheric synchronization patterns linked with shooting performance in archers. Behav Brain Res 2024; 460:114813. [PMID: 38110123 DOI: 10.1016/j.bbr.2023.114813] [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: 08/15/2023] [Revised: 12/10/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023]
Abstract
Sustainable attention, effective visual-spatial perception, and motor control skills are considered highly important for achieving superior athletic performance. The aim of the current study was to investigate hemispheric synchronization patterns of brain electrical activation related to successful and unsuccessful shots of archers using electroencephalography (EEG). This study involved 16 elite archers, each shooting 36 arrows. The 10 shots closest to the target's center were successful, while the 10 farthest shots were unsuccessful. The transformed EEG data, obtained through surface Laplacian filtering, were divided into 5 sub-bands (theta, alpha1, alpha2, beta1, beta2) by calculating the alpha peak frequencies. The synchronization values of the electrode pairs were calculated using the Phase Locking Value (PLV) method. To compare the EEG data for successful and unsuccessful shots in all frequency bands, the linear mixed models were fitted. Perceived fatigue levels were quantified using a visual analog scale (VAS). Spearman's correlation analysis was conducted to examine the relationship between fatigue and shooting performance. The results showed significantly higher coupling strength for C3-O1, C4-O2, O1-O2, F3-F4, C4-T8, T7-O2, F4-C4, C3-O2 and F4-T8 pairs during successful shooting. Moreover, the coupling strengths for F3-O2, F4-T7, C3-C4, C3-T8, T7-T8, C4-O1, F3-T8, and F4-O2 were significantly higher in unsuccessful shooting. The current findings revealed differences in the synchronization patterns associated with shooting performance. It is observed that visual-motor performance is correlated with an increase in cortical synchronization values during successful shots. These findings have the potential to serve as a theoretical reference that contributes to superior performance.
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Affiliation(s)
- Hasan Batuhan Dirik
- Eskisehir Technical University, Department of Movement and Training Sciences, Faculty of Sport Sciences, Eskisehir, TURKEY.
| | - Hayri Ertan
- Eskisehir Technical University, Department of Movement and Training Sciences, Faculty of Sport Sciences, Eskisehir, TURKEY
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2
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Luo Y, Li J, Zhang Y, Pan W. The scalp prefrontal-limbic functional connectivity moderates stress-related rumination effects on stress recovery. Psychophysiology 2024; 61:e14462. [PMID: 37990390 DOI: 10.1111/psyp.14462] [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: 05/11/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND Mood disorders are often associated with hypothalamic-pituitary-adrenal (HPA) axis dysfunction, and rumination has been implicated in delayed cortisol recovery. However, research findings on the impact of rumination on cortisol recovery have been inconsistent. The moderating effects of scalp prefrontal-limbic connections on the relationship between rumination and cortisol recovery may explain these discrepancies. METHOD Acute stress was induced by a 5-min simulated job interview. Salivary samples and affective ratings were collected at seven pre-determined time points. After the simulated job interview, 35 healthy adult participants were randomly assigned to either the rumination condition (n = 17) or the distraction condition (n = 18). RESULTS Inducing stress and rumination led to increased cortisol levels, negative mood, and state rumination. Compared with the distraction group, the rumination group displayed delayed cortisol recovery and decreased scalp prefrontal-limbic connectivities, that is, left ventrolateral prefrontal cortex (LVLPFC) and left temporal area (LTMP) [ps < .05], and right dorsolateral prefrontal cortex (RDLPFC) and anterior cingulate cortex (ACC) [ps < .05]. The relationship between rumination and cortisol recovery was moderated by connectivities between the left dorsolateral prefrontal cortex (LDLPFC) and LTMP, RDLPFC and LTMP, LDLPFC and ACC, and RDLPFC and ACC [B = -0.98 to -0.35, SE = 0.15-0.34, ps < .05]. Higher rumination combined with reduced scalp prefrontal-limbic connectivities to predict delayed cortisol recovery. CONCLUSION The current findings suggest that scalp prefrontal-limbic connectivity is a neural underpinning related to emotion regulation for the effects of state rumination on stress recovery. These findings also provide a potential target for non-invasive intervention in HPA axis dysregulation.
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Affiliation(s)
- Yu Luo
- School of Psychology, Guizhou Normal University, Guiyang, China
| | - Jinjin Li
- School of Psychology, Guizhou Normal University, Guiyang, China
| | - Yu Zhang
- School of Psychology, Guizhou Normal University, Guiyang, China
| | - Wenhao Pan
- School of Public Administration, South China University of Technology, Guangzhou, China
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3
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Hasan MA, Sattar P, Qazi SA, Fraser M, Vuckovic A. Brain Networks With Modified Connectivity in Patients With Neuropathic Pain and Spinal Cord Injury. Clin EEG Neurosci 2024; 55:88-100. [PMID: 34714181 DOI: 10.1177/15500594211051485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background. Neuropathic pain (NP) following spinal cord injury (SCI) affects the quality of life of almost 40% of the injured population. The modified brain connectivity was reported under different NP conditions. Therefore, brain connectivity was studied in the SCI population with and without NP with the aim to identify networks that are altered due to injury, pain, or both. Methods. The study cohort is classified into 3 groups, SCI patients with NP, SCI patients without NP, and able-bodied. EEG of each participant was recorded during motor imagery (MI) of paralyzed and painful, and nonparalyzed and nonpainful limbs. Phased locked value was calculated using Hilbert transform to study altered functional connectivity between different regions. Results. The posterior region connectivity with frontal, fronto-central, and temporal regions is strongly decreased mainly during MI of dominant upper limb (nonparalyzed and nonpainful limbs) in SCI no pain group. This modified connectivity is prominent in the alpha and high-frequency bands (beta and gamma). Moreover, oscillatory modified global connectivity is observed in the pain group during MI of painful and paralyzed limb which is more evident between fronto-posterior, frontocentral-posterior, and within posterior and within frontal regions in the theta and SMR frequency bands. Cluster coefficient and local efficiency values are reduced in patients with no reported pain group while increased in the PWP group. Conclusion. The altered theta band connectivity found in the fronto-parietal network along with a global increase in local efficiency is a consequence of pain only, while altered connectivity in the beta and gamma bands along with a decrease in cluster coefficient values observed in the sensory-motor network is dominantly a consequence of injury only. The outcomes of this study may be used as a potential diagnostic biomarker for the NP. Further, the expected insight holds great clinical relevance in the design of neurofeedback-based neurorehabilitation and connectivity-based brain-computer interfaces for SCI patients.
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Affiliation(s)
- Muhammad A Hasan
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Parisa Sattar
- Neurocomputation Laboratory, National Centre for Artificial Intelligence, Karachi, Pakistan
| | - Saad A Qazi
- Neurocomputation Laboratory, National Centre for Artificial Intelligence, Karachi, Pakistan
- Department of Electrical and Computer Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Matthew Fraser
- Queen Elizabeth National Spinal Unit, Southern General Hospital, Glasgow, UK
| | - Aleksandra Vuckovic
- Centre for Rehabilitation Engineering, School of Engineering, University of Glasgow, Glasgow, UK
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Shin Y, Hwang S, Lee SB, Son H, Chu K, Jung KY, Lee SK, Park KI, Kim YG. Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning. Sci Rep 2023; 13:22532. [PMID: 38110465 PMCID: PMC10728218 DOI: 10.1038/s41598-023-49255-2] [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: 05/17/2023] [Accepted: 12/06/2023] [Indexed: 12/20/2023] Open
Abstract
Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.
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Affiliation(s)
- Youmin Shin
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Interdisciplinary Program in Bio-Engineering, Seoul National University, Seoul, Korea
| | - Sungeun Hwang
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Hyoshin Son
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kon Chu
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki-Young Jung
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sang Kun Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyung-Il Park
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea.
| | - Young-Gon Kim
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.
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Rogala J, Żygierewicz J, Malinowska U, Cygan H, Stawicka E, Kobus A, Vanrumste B. Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis. Sci Rep 2023; 13:21748. [PMID: 38066046 PMCID: PMC10709647 DOI: 10.1038/s41598-023-49048-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.
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Affiliation(s)
- Jacek Rogala
- Faculty of Physics, University of Warsaw, Pasteura 5, 02-093, Warsaw, Poland.
| | | | - Urszula Malinowska
- Faculty of Physics, University of Warsaw, Pasteura 5, 02-093, Warsaw, Poland
| | - Hanna Cygan
- Institute of Physiology and Pathology of Hearing, Bioimaging Research Center, World Hearing Center, Warsaw, Poland
| | - Elżbieta Stawicka
- Clinic of Paediatric Neurology, Institute of Mother and Child, Kasprzaka 17A, 01-211, Warsaw, Poland
| | - Adam Kobus
- Institute of Computer Science, Marie Curie-Skłodowska University, Pl. M. Curie-Skłodowskiej 1, 20-031, Lublin, Poland
| | - Bart Vanrumste
- Department of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
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Long T, Wan M, Jian W, Dai H, Nie W, Xu J. Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset. Front Hum Neurosci 2023; 17:1143027. [PMID: 37056962 PMCID: PMC10089123 DOI: 10.3389/fnhum.2023.1143027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/03/2023] [Indexed: 03/30/2023] Open
Abstract
IntroductionThe volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system.MethodsDataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction.ResultsExperiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols.DiscussionOur work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.
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Affiliation(s)
- Taixue Long
- The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
| | - Min Wan
- The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Wenjuan Jian
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
- *Correspondence: Wenjuan Jian
| | - Honghui Dai
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
| | - Wenbing Nie
- The Army Infantry College of PLA, Nanchang, Jiangxi, China
| | - Jianzhong Xu
- The Army Infantry College of PLA, Nanchang, Jiangxi, China
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Zheng F, Hu B, Zheng X, Ji C, Bian J, Yu X. Dynamic differential entropy and brain connectivity features based EEG emotion recognition. INT J INTELL SYST 2022. [DOI: 10.1002/int.23096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Fa Zheng
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
| | - Bin Hu
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
| | - Xiangwei Zheng
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
| | - Cun Ji
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
| | - Ji Bian
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
| | - Xiaomei Yu
- School of Information Science and Engineering Shandong Normal University Jinan China
- Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China
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8
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Droby A, Nosatzki S, Edry Y, Thaler A, Giladi N, Mirelman A, Maidan I. The interplay between structural and functional connectivity in early stage Parkinson's disease patients. J Neurol Sci 2022; 442:120452. [PMID: 36265263 DOI: 10.1016/j.jns.2022.120452] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/21/2022] [Accepted: 10/04/2022] [Indexed: 10/31/2022]
Abstract
The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.
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Affiliation(s)
- Amgad Droby
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Shai Nosatzki
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
| | - Yariv Edry
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
| | - Avner Thaler
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Nir Giladi
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Karimi F, Almeida Q, Jiang N. Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson's disease? Front Aging Neurosci 2022; 14:988037. [PMID: 36389071 PMCID: PMC9643859 DOI: 10.3389/fnagi.2022.988037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/03/2022] [Indexed: 08/18/2023] Open
Abstract
Freezing of gait (FOG) is a complex gait disturbance in Parkinson's disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.
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Affiliation(s)
- Fatemeh Karimi
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Quincy Almeida
- Movement Disorders Research and Rehabilitation Consortium, Department of Kinesiology and Physical Education, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Ning Jiang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Manufacturing, Sichuan University, Chengdu, China
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Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study. Int J Psychophysiol 2022; 175:43-53. [PMID: 35217110 DOI: 10.1016/j.ijpsycho.2022.02.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/19/2021] [Accepted: 02/17/2022] [Indexed: 11/20/2022]
Abstract
Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4-8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups of participants. These differences can help explain the underlying processes involved in these cognitive disorders and the differences between aMCI and AD patients in performing cognitive tasks. Our study also proposes a diagnosis method for aMCI through comparing the brain's response characteristics during olfactory stimulation and rest. Early diagnosis of aMCI can potentially lead to its timely treatment and prevention from progression to AD.
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Rogala J, Dreszer J, Malinowska U, Waligóra M, Pluta A, Antonova I, Wróbel A. Stronger connectivity and higher extraversion protect against stress-related deterioration of cognitive functions. Sci Rep 2021; 11:17452. [PMID: 34465808 PMCID: PMC8408208 DOI: 10.1038/s41598-021-96718-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/13/2021] [Indexed: 11/09/2022] Open
Abstract
Here we attempted to define the relationship between: EEG activity, personality and coping during lockdown. We were in a unique situation since the COVID-19 outbreak interrupted our independent longitudinal study. We already collected a significant amount of data before lockdown. During lockdown, a subgroup of participants willingly continued their engagement in the study. These circumstances provided us with an opportunity to examine the relationship between personality/cognition and brain rhythms in individuals who continued their engagement during lockdown compared to control data collected well before pandemic. The testing consisted of a one-time assessment of personality dimensions and two sessions of EEG recording and deductive reasoning task. Participants were divided into groups based on the time they completed the second session: before or during the COVID-19 outbreak ‘Pre-pandemic Controls’ and ‘Pandemics’, respectively. The Pandemics were characterized by a higher extraversion and stronger connectivity, compared to Pre-pandemic Controls. Furthermore, the Pandemics improved their cognitive performance under long-term stress as compared to the Pre-Pandemic Controls matched for personality traits to the Pandemics. The Pandemics were also characterized by increased EEG connectivity during lockdown. We posit that stronger EEG connectivity and higher extraversion could act as a defense mechanism against stress-related deterioration of cognitive functions.
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Affiliation(s)
- Jacek Rogala
- Bioimaging Research Center, World Hearing Center, Institute of Physiology and Pathology of Hearing, Warsaw, Poland. .,The Center for Systemic Risk Analysis, Faculty of "Artes Liberales", University of Warsaw, Warsaw, Poland.
| | - Joanna Dreszer
- The Center for Systemic Risk Analysis, Faculty of "Artes Liberales", University of Warsaw, Warsaw, Poland.,Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Toruń, Toruń, Poland
| | - Urszula Malinowska
- Instytut Biologii Doświadczalnej Im. Marcelego Nenckiego, Warsaw, Poland
| | - Marek Waligóra
- Instytut Biologii Doświadczalnej Im. Marcelego Nenckiego, Warsaw, Poland
| | - Agnieszka Pluta
- Faculty of Psychology, The University of Warsaw, Warsaw, Poland
| | - Ingrida Antonova
- Instytut Biologii Doświadczalnej Im. Marcelego Nenckiego, Warsaw, Poland
| | - Andrzej Wróbel
- The Center for Systemic Risk Analysis, Faculty of "Artes Liberales", University of Warsaw, Warsaw, Poland.,Instytut Biologii Doświadczalnej Im. Marcelego Nenckiego, Warsaw, Poland.,Institute of Philosophy, Faculty of Epistemology, University of Warsaw, Warsaw, Poland
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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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A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.
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14
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Training state and performance evaluation of working memory based on task-related EEG. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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15
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Gong A, Liu J, Lu L, Wu G, Jiang C, Fu Y. Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Minati L, Yoshimura N, Frasca M, Drożdż S, Koike Y. Warped phase coherence: An empirical synchronization measure combining phase and amplitude information. CHAOS (WOODBURY, N.Y.) 2019; 29:021102. [PMID: 30823716 DOI: 10.1063/1.5082749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 01/21/2019] [Indexed: 06/09/2023]
Abstract
The entrainment between weakly coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms of whether a stable relationship is detectable between the instantaneous phases extracted from the measured or simulated time-series via the analytic signal. Here, we demonstrate that adding a possibly complex constant value to this normally null-mean signal has a non-trivial warping effect. Among other consequences, this introduces a level of sensitivity to the amplitude fluctuations and average relative phase. By means of simulations of Rössler systems and experiments on single-transistor oscillator networks, it is shown that the resulting coherence measure may have an empirical value in improving the inference of the structural couplings from the dynamics. When tentatively applied to the electroencephalogram recorded while performing imaginary and real movements, this straightforward modification of the phase locking value substantially improved the classification accuracy. Hence, its possible practical relevance in brain-computer and brain-machine interfaces deserves consideration.
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Affiliation(s)
- Ludovico Minati
- Tokyo Tech World Research Hub Initiative-Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Natsue Yoshimura
- FIRST-Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Mattia Frasca
- Department of Electrical Electronic and Computer Engineering (DIEEI), University of Catania, 95131 Catania, Italy
| | - Stanisław Drożdż
- Complex Systems Theory Department, Institute of Nuclear Physics-Polish Academy of Sciences (IFJ-PAN), 31-342 Kraków, Poland
| | - Yasuharu Koike
- FIRST-Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
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Hua C, Wang H, Wang H, Lu S, Liu C, Khalid SM. A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection. Int J Neural Syst 2019; 29:1850015. [DOI: 10.1142/s0129065718500156] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).
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Affiliation(s)
- Chengcheng Hua
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Hong Wang
- Control System Centre, The University of Manchester, Manchester, UK
| | - Shaowen Lu
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110189, P. R. China
| | - Chong Liu
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Syed Madiha Khalid
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
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Hong J, Qin X, Li J, Niu J, Wang W. Signal processing algorithms for motor imagery brain-computer interface: State of the art. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-181309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jie Hong
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Xiansheng Qin
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Jing Li
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Junlong Niu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Wenjie Wang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
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Chouhan T, Robinson N, Vinod AP, Ang KK, Guan C. Wavlet phase-locking based binary classification of hand movement directions from EEG. J Neural Eng 2018; 15:066008. [PMID: 30181429 DOI: 10.1088/1741-2552/aadeed] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions. APPROACH In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions. EEG data collected from seven subjects performing right hand movements in four orthogonal directions in the horizontal plane is used for this analysis. MAIN RESULTS Our proposed W-PLV based method achieves a mean binary classification accuracy of 76.85% over seven subjects using wavelet levels corresponding to ⩽12 Hz EEG. The results also show direction-dependent information in various wavelet levels and indicate the presence of relevant information in slow cortical potentials (<1 Hz) as well as higher wavelet levels (⩽12 Hz). SIGNIFICANCE This study presents a thorough analysis of the phase-locking patterns extracted from EEG corresponding to hand movements in different directions using W-PLV across various wavelet levels and verifies their discriminative ability in the single trial binary classification of hand movement directions.
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Affiliation(s)
- Tushar Chouhan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
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Abstract
Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.
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Affiliation(s)
- D J McFarland
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - J R Wolpaw
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA
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