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Pedapati EV, Ethridge LE, Liu Y, Liu R, Sweeney JA, DeStefano LA, Miyakoshi M, Razak K, Schmitt LM, Moore DR, Gilbert DL, Wu SW, Smith E, Shaffer RC, Dominick KC, Horn PS, Binder D, Erickson CA. Frontal Cortex Hyperactivation and Gamma Desynchrony in Fragile X Syndrome: Correlates of Auditory Hypersensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598957. [PMID: 38915683 PMCID: PMC11195233 DOI: 10.1101/2024.06.13.598957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Fragile X syndrome (FXS) is an X-linked disorder that often leads to intellectual disability, anxiety, and sensory hypersensitivity. While sound sensitivity (hyperacusis) is a distressing symptom in FXS, its neural basis is not well understood. It is postulated that hyperacusis may stem from temporal lobe hyperexcitability or dysregulation in top-down modulation. Studying the neural mechanisms underlying sound sensitivity in FXS using scalp electroencephalography (EEG) is challenging because the temporal and frontal regions have overlapping neural projections that are difficult to differentiate. To overcome this challenge, we conducted EEG source analysis on a group of 36 individuals with FXS and 39 matched healthy controls. Our goal was to characterize the spatial and temporal properties of the response to an auditory chirp stimulus. Our results showed that males with FXS exhibit excessive activation in the frontal cortex in response to the stimulus onset, which may reflect changes in top-down modulation of auditory processing. Additionally, during the chirp stimulus, individuals with FXS demonstrated a reduction in typical gamma phase synchrony, along with an increase in asynchronous gamma power, across multiple regions, most strongly in temporal cortex. Consistent with these findings, we observed a decrease in the signal-to-noise ratio, estimated by the ratio of synchronous to asynchronous gamma activity, in individuals with FXS. Furthermore, this ratio was highly correlated with performance in an auditory attention task. Compared to controls, males with FXS demonstrated elevated bidirectional frontotemporal information flow at chirp onset. The evidence indicates that both temporal lobe hyperexcitability and disruptions in top-down regulation play a role in auditory sensitivity disturbances in FXS. These findings have the potential to guide the development of therapeutic targets and back-translation strategies.
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Affiliation(s)
- Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lauren E Ethridge
- Department of Pediatrics, Section on Developmental and Behavioral Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Department of Psychology, University of Oklahoma, Norman, OK, United States
| | - Yanchen Liu
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Rui Liu
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lisa A DeStefano
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Makoto Miyakoshi
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Khaleel Razak
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Lauren M Schmitt
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - David R Moore
- Communication Sciences Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Manchester Centre for Audiology and Deafness, University of Manchester, Manchester, UK
| | - Donald L Gilbert
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Steve W Wu
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Elizabeth Smith
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Rebecca C Shaffer
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Kelli C Dominick
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Paul S Horn
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Devin Binder
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, United States
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Siviero I, Menegaz G, Storti SF. Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance. SENSORS (BASEL, SWITZERLAND) 2023; 23:7520. [PMID: 37687976 PMCID: PMC10490741 DOI: 10.3390/s23177520] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023]
Abstract
(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
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Affiliation(s)
- Ilaria Siviero
- Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Silvia Francesca Storti
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
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Zhu JY, Li MM, Zhang ZH, Liu G, Wan H. Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions. ENTROPY (BASEL, SWITZERLAND) 2023; 25:994. [PMID: 37509941 PMCID: PMC10378602 DOI: 10.3390/e25070994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory-spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual-spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4-12 Hz) in visual-spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.
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Affiliation(s)
- Jun-Yao Zhu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Meng-Meng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhi-Heng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Gang Liu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Hong Wan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
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Lueckel JM, Upadhyay N, Purrer V, Maurer A, Borger V, Radbruch A, Attenberger U, Wuellner U, Panda R, Boecker H. Whole-brain network transitions within the framework of ignition and transfer entropy following VIM-MRgFUS in essential tremor patients. Brain Stimul 2023; 16:879-888. [PMID: 37230462 DOI: 10.1016/j.brs.2023.05.006] [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: 10/28/2022] [Revised: 03/30/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Magnetic resonance-guided focused ultrasound (MRgFUS) lesioning of the ventralis intermedius nucleus (VIM) has shown promise in treating drug-refractory essential tremor (ET). It remains unknown whether focal VIM lesions by MRgFUS have broader restorative effects on information flow within the whole-brain network of ET patients. We applied an information-theoretical approach based on intrinsic ignition and the concept of transfer entropy (TE) to assess the spatiotemporal dynamics after VIM-MRgFUS. Eighteen ET patients (mean age 71.44 years) underwent repeated 3T resting-state functional magnetic resonance imaging combined with Clinical Rating Scale for Tremor (CRST) assessments one day before (T0) and one month (T1) and six months (T2) post-MRgFUS, respectively. We observed increased whole brain ignition-driven mean integration (IDMI) at T1 (p < 0.05), along with trend increases at T2. Further, constraining to motor network nodes, we identified significant increases in information-broadcasting (bilateral supplementary motor area (SMA) and left cerebellar lobule III) and information-receiving (right precentral gyrus) at T1. Remarkably, increased information-broadcasting in bilateral SMA was correlated with relative improvement of the CRST in the treated hand. In addition, causal TE-based effective connectivity (EC) at T1 showed an increase from right SMA to left cerebellar lobule crus II and from left cerebellar lobule III to right thalamus. In conclusion, results suggest a change in information transmission capacity in ET after MRgFUS and a shift towards a more integrated functional state with increased levels of global and directional information flow.
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Affiliation(s)
- Julia M Lueckel
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
| | - Neeraj Upadhyay
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Veronika Purrer
- German Center for Neurodegenerative Diseases, Bonn, Germany; Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Angelika Maurer
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Valeri Borger
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Alexander Radbruch
- German Center for Neurodegenerative Diseases, Bonn, Germany; Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Ullrich Wuellner
- German Center for Neurodegenerative Diseases, Bonn, Germany; Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Henning Boecker
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases, Bonn, Germany.
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Cardona-Álvarez YN, Álvarez-Meza AM, Cárdenas-Peña DA, Castaño-Duque GA, Castellanos-Dominguez G. A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments. SENSORS (BASEL, SWITZERLAND) 2023; 23:3763. [PMID: 37050823 PMCID: PMC10098804 DOI: 10.3390/s23073763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.
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Affiliation(s)
| | | | | | - Germán Albeiro Castaño-Duque
- Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
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Zhou W, Yu S, Chen B. Causality detection with matrix-based transfer entropy. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wu Z, Chen X, Gao M, Hong M, He Z, Hong H, Shen J. Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M. Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network. SENSORS (BASEL, SWITZERLAND) 2021; 21:6570. [PMID: 34640888 PMCID: PMC8512774 DOI: 10.3390/s21196570] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/24/2022]
Abstract
Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
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Affiliation(s)
- Muhammad Ahsan Awais
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mohd Zuki Yusoff
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Danish M. Khan
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
- Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan
| | - Norashikin Yahya
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Nidal Kamel
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mansoor Ebrahim
- Faculty of Engineering, Sciences, and Technology, Iqra University, Karachi 75500, Pakistan;
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Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks. SENSORS 2021; 21:s21082750. [PMID: 33924672 PMCID: PMC8069819 DOI: 10.3390/s21082750] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/01/2021] [Accepted: 04/08/2021] [Indexed: 02/06/2023]
Abstract
Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.
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