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Liang Z, Li S, Zhou S, Chen S, Li Y, Chen Y, Zhao Q, Huang F, Lu C, Yu Q, Zhou Z. Increased or decreased? Interpersonal neural synchronization in group creation. Neuroimage 2022; 260:119448. [PMID: 35843516 DOI: 10.1016/j.neuroimage.2022.119448] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 07/01/2022] [Accepted: 07/03/2022] [Indexed: 10/17/2022] Open
Abstract
Group creation is the process by which group members collaborate to produce novel and useful ideas or products, including ideas generation and evaluation. However, the interpersonal neural mechanism of group creation during natural communication remains unclear. In this study, two groups of same-sex dyads with similar individual creativity collaborated to complete the Product Improvement Task (creative condition) and the Item Purchase Plan Task (control condition), respectively. Functional near-infrared spectroscopy (fNIRS) was used to record both members' neural activity in the left prefrontal (lPFC) and right temporal-parietal junction (rTPJ) regions during the task. Considering that the role asymmetry of group members may have an impact on interpersonal neural patterns, we identified leaders and followers in the dyads based on participant performance. The results showed that leaders and followers in the creative condition had significantly lower interpersonal neural synchronization (INS) in the right superior temporal gyrus-left superior frontal gyrus, right supramarginal gyrus-left superior frontal gyrus, and right supramarginal gyrus-left middle frontal gyrus than in the control condition. Partial multivariate Granger causality analyses revealed the influence between dyads was bidirectional but was significantly stronger from the leaders to the followers than the other direction. In addition, in the creative task, the INS was significantly associated with novelty, appropriateness, and conflict of views. All these findings suggest that the ideas generation and ideas evaluation process in group creation have poor interpersonal neural activity coupling due to factors such as the difficulty of understanding novel ideas. However, performances may be improved when groups can better integrate views and reach collective understanding, intentions, and goals. Furthermore, we found that there are differences in the dynamics of INS in different brain regions. The INS related to the novelty of the group creation decreased in the early stages, while the INS related to the appropriateness decreased in the middle stages. Our findings reveal a unique interpersonal neural pattern of group creation processes in the context of natural communication.
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Affiliation(s)
- Zheng Liang
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China
| | - Songqing Li
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China; College of Electronic Engineering, Naval University of Engineering, Wuhan, China
| | - Siyuan Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Shi Chen
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China
| | - Ying Li
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China; School of Preschool Education, Changsha Normal University, Changsha, China
| | - Yanran Chen
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China
| | - Qingbai Zhao
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China.
| | - Furong Huang
- School of Psychology, Jiangxi Normal University, Nanchang, China.
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Quanlei Yu
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China.
| | - Zhijin Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Central China Normal University, Wuhan, China; Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China.
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Bonaiuto JJ, Little S, Neymotin SA, Jones SR, Barnes GR, Bestmann S. Laminar dynamics of high amplitude beta bursts in human motor cortex. Neuroimage 2021; 242:118479. [PMID: 34407440 PMCID: PMC8463839 DOI: 10.1016/j.neuroimage.2021.118479] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 12/28/2022] Open
Abstract
Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
| | - Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, RI, USA
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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Koutlis C, Kugiumtzis D. The Effect of a Hidden Source on the Estimation of Connectivity Networks from Multivariate Time Series. ENTROPY 2021; 23:e23020208. [PMID: 33567755 PMCID: PMC7915465 DOI: 10.3390/e23020208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure has been assessed using simulated dynamical systems where the ground truth is known. Here, we consider the presence of an unobserved variable that acts as a hidden source for the observed high-dimensional dynamical system and study the effect of the hidden source on the estimation of the connectivity structure. In particular, the focus is on estimating the direct causality effects in high-dimensional time series (not including the hidden source) of relatively short length. We examine the performance of a linear and a nonlinear connectivity measure using dimension reduction and compare them to a linear measure designed for latent variables. For the simulations, four systems are considered, the coupled Hénon maps system, the coupled Mackey-Glass system, the neural mass model and the vector autoregressive (VAR) process, each comprising 25 subsystems (variables for VAR) at close chain coupling structure and another subsystem (variable for VAR) driving all others acting as the hidden source. The results show that the direct causality measures estimate, in general terms, correctly the existing connectivity in the absence of the source when its driving is zero or weak, yet fail to detect the actual relationships when the driving is strong, with the nonlinear measure of dimension reduction performing best. An example from finance including and excluding the USA index in the global market indices highlights the different performance of the connectivity measures in the presence of hidden source.
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Affiliation(s)
- Christos Koutlis
- Information Technologies Institute, Centre of Research and Technology Hellas, 57001 Thessaloniki, Greece;
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece
- Correspondence: ; Tel.: +30-2310995955
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Fahimi Hnazaee M, Khachatryan E, Chehrazad S, Kotarcic A, De Letter M, Van Hulle MM. Overlapping connectivity patterns during semantic processing of abstract and concrete words revealed with multivariate Granger Causality analysis. Sci Rep 2020; 10:2803. [PMID: 32071356 PMCID: PMC7028761 DOI: 10.1038/s41598-020-59473-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 01/29/2020] [Indexed: 11/18/2022] Open
Abstract
. Abstract, unlike concrete, nouns refer to notions beyond our perception. Even though there is no consensus among linguists as to what exactly constitutes a concrete or abstract word, neuroscientists found clear evidence of a "concreteness" effect. This can, for instance, be seen in patients with language impairments due to brain injury or developmental disorder who are capable of perceiving one category better than another. Even though the results are inconclusive, neuroimaging studies on healthy subjects also provide a spatial and temporal account of differences in the processing of abstract versus concrete words. A description of the neural pathways during abstract word reading, the manner in which the connectivity patterns develop over the different stages of lexical and semantic processing compared to that of concrete word processing are still debated. We conducted a high-density EEG study on 24 healthy young volunteers using an implicit categorization task. From this, we obtained high spatio-temporal resolution data and, by means of source reconstruction, reduced the effect of signal mixing observed on scalp level. A multivariate, time-varying and directional method of analyzing connectivity based on the concept of Granger Causality (Partial Directed Coherence) revealed a dynamic network that transfers information from the right superior occipital lobe along the ventral and dorsal streams towards the anterior temporal and orbitofrontal lobes of both hemispheres. Some regions along these pathways appear to be primarily involved in either receiving or sending information. A clear difference in information transfer of abstract and concrete words was observed during the time window of semantic processing, specifically for information transferred towards the left anterior temporal lobe. Further exploratory analysis confirmed a generally stronger connectivity pattern for processing concrete words. We believe our study could guide future research towards a more refined theory of abstract word processing in the brain.
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Affiliation(s)
- Mansoureh Fahimi Hnazaee
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
| | - Elvira Khachatryan
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Sahar Chehrazad
- Numerical Analysis and Applied Mathematics Section, Department of Computer Science, KU Leuven, Leuven, Belgium
| | - Ana Kotarcic
- Center for the Historiography of Linguistics, Department of Comparative, Historical and Applied Linguistics, KU Leuven, Leuven, Belgium
| | - Miet De Letter
- Medicine and Health Sciences, Department of Rehabilitation Sciences, UGent, Gent, Belgium
| | - Marc M Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
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Using Partial Directed Coherence to Study Alpha-Band Effective Brain Networks during a Visuospatial Attention Task. Behav Neurol 2019; 2019:1410425. [PMID: 31565094 PMCID: PMC6745104 DOI: 10.1155/2019/1410425] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 05/20/2019] [Accepted: 06/10/2019] [Indexed: 12/29/2022] Open
Abstract
Previous studies have shown that the neural mechanisms underlying visual spatial attention rely on top-down control information from the frontal and parietal cortexes, which ultimately amplifies sensory processing of stimulus occurred at the attended location relative to those at unattended location. However, the modulations of effective brain networks in response to stimulus at attended and unattended location are not yet clear. In present study, we collected event-related potentials (ERPs) from 15 subjects during a visual spatial attention task, and a partial directed coherence (PDC) method was used to construct alpha-band effective brain networks of two conditions (targets at attended and nontargets at unattended location). Flow gain mapping, effective connectivity pattern, and graph measures including clustering coefficient (C), characteristic path length (L), global efficiency (Eglobal), and local efficiency (Elocal) were compared between two conditions. Flow gain mapping showed that the frontal region seemed to serve as the main source of information transmission in response to targets at attended location while the parietal region served as the main source in nontarget condition. Effective connectivity pattern indicated that in response to targets, there existed obvious top-down connections from the frontal, temporal, and parietal cortexes to the visual cortex compared with in response to nontargets. Graph theory analysis was used to quantify the topographical properties of the brain networks, and results revealed that in response to targets, the brain networks were characterized by significantly smaller characteristic path length and larger global efficiency than in response to nontargets. Our findings suggested that smaller characteristic path length and larger global efficiency could facilitate global integration of information and provide a substrate for more efficient perceptual processing of targets at attended location compared with processing of nontargets at ignored location, which revealed the neural mechanisms underlying visual spatial attention from the perspective of effective brain networks and graph theory for the first time and opened new vistas to interpret a cognitive process.
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Nunes RV, Reyes MB, de Camargo RY. Evaluation of connectivity estimates using spiking neuronal network models. BIOLOGICAL CYBERNETICS 2019; 113:309-320. [PMID: 30783758 DOI: 10.1007/s00422-019-00796-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 02/08/2019] [Indexed: 06/09/2023]
Abstract
The flow of information between different regions of the cortex is fundamental for brain function. Researchers use causality detection techniques, such as Granger causality, to infer connectivity among brain areas from time series. Generalized partial directed coherence (GPDC) is a frequency domain linear method based on vector autoregressive model, which has been applied in electroencephalography, local field potential, and blood oxygenation level-dependent signals. Despite its widespread usage, previous attempts to validate GPDC use oversimplified simulated data, which do not reflect the nonlinearities and network couplings present in biological signals. In this work, we evaluated the GPDC performance when applied to simulated LFP signals, i.e., generated from networks of spiking neuronal models. We created three models, each containing five interacting networks, and evaluated whether the GPDC method could accurately detect network couplings. When using a stronger coupling, we showed that GPDC correctly detects all existing connections from simulated LFP signals in the three models, without false positives. Varying the coupling strength between networks, by changing the number of connections or synaptic strengths, and adding noise in the times series, altered the receiver operating characteristic (ROC) curve, ranging from perfect to chance level retrieval. We also showed that GPDC values correlated with coupling strength, indicating that GPDC values can provide useful information regarding coupling strength. These results reinforce that GPDC can be used to detect causality relationships over neural signals.
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Affiliation(s)
- Ronaldo V Nunes
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
| | - Marcelo B Reyes
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Raphael Y de Camargo
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
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Koutlis C, Kimiskidis VK, Kugiumtzis D. Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series. Int J Neural Syst 2019; 29:1850051. [DOI: 10.1142/s012906571850051x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The study of connectivity patterns of a system’s variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality. We term the proposed advanced method, PMIME0. The estimation of instantaneous causality by PMIME0 is a signature of the presence of hidden source in the observed system, as demonstrated analytically in a toy model. It is further demonstrated that the PMIME0 identifies the true instantaneous with great accuracy in a variety of high-dimensional dynamical systems. The method is applied to EEG data with epileptiform discharges (EDs), and the results imply a strong impact of unobserved confounders during the EDs. This finding comes as a possible explanation for the increased levels of causality during epileptic seizures estimated by some measures affected by the presence of a common source.
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Affiliation(s)
- Christos Koutlis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Vasilios K. Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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Li Y, Lei M, Cui W, Guo Y, Wei HL. A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs. IEEE Trans Biomed Eng 2019; 66:3509-3525. [PMID: 30932821 DOI: 10.1109/tbme.2019.2906688] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. METHODS The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. RESULTS The proposed method is validated on two simulations, and then applied to source level motor imagery (MI) EEGs, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEGs are physiologically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. CONCLUSION Experimental results confirm the effectiveness of the TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. SIGNIFICANCE The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition.
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Wong-Lin K, Wang DH, Moustafa AA, Cohen JY, Nakamura K. Toward a multiscale modeling framework for understanding serotonergic function. J Psychopharmacol 2017; 31:1121-1136. [PMID: 28417684 PMCID: PMC5606304 DOI: 10.1177/0269881117699612] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin.
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Affiliation(s)
- KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Derry~Londonderry, UK
| | - Da-Hui Wang
- School of Systems Science, and National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ahmed A Moustafa
- School of Social Sciences and Psychology, and Marcs Institute for Brain and Behaviour, University of Western Sydney, Sydney, Australia
| | - Jeremiah Y Cohen
- Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Kae Nakamura
- Department of Physiology, Kansai Medical University, Hirakata, Osaka, Japan
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Rathee D, Cecotti H, Prasad G. Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks. J Neural Eng 2017; 14:056005. [PMID: 28597846 DOI: 10.1088/1741-2552/aa785c] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. APPROACH We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. MAIN RESULTS The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. SIGNIFICANCE We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.
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Affiliation(s)
- Dheeraj Rathee
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Ulster University, Derry-Londonderry, United Kingdom
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Huang D, Ren A, Shang J, Lei Q, Zhang Y, Yin Z, Li J, von Deneen KM, Huang L. Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks. Front Hum Neurosci 2016; 10:235. [PMID: 27242495 PMCID: PMC4873517 DOI: 10.3389/fnhum.2016.00235] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2015] [Accepted: 05/04/2016] [Indexed: 11/13/2022] Open
Abstract
PURPOSE The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. METHODS AND MATERIALS EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. RESULTS The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. SIGNIFICANCE This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.
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Affiliation(s)
- Dengfeng Huang
- School of Life Science and Technology, Xidian UniversityXi'an, China
- Department of Radiology, Medical Physics, University Medical Center FreiburgFreiburg, Germany
| | - Aifeng Ren
- School of Electronic Engineering, Xidian UniversityXi'an, China
| | - Jing Shang
- School of Electronic Engineering, Xidian UniversityXi'an, China
| | - Qiao Lei
- School of Life Science and Technology, Xidian UniversityXi'an, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian UniversityXi'an, China
| | - Zhongliang Yin
- School of Life Science and Technology, Xidian UniversityXi'an, China
| | - Jun Li
- School of Life Science and Technology, Xidian UniversityXi'an, China
| | | | - Liyu Huang
- School of Life Science and Technology, Xidian UniversityXi'an, China
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