Neural decoding of continuous upper limb movements: a meta-analysis.

EEG-based motion trajectory decoding makes a promising approach for neurotechnology which can be used for neural control of motion reconstruction and neurorehabilitation tools. However, the feasibility and validity of continuous motion decoding by non-invasive brain activity are not clear. The main...

Descripción completa

Detalles Bibliográficos
Publicado en:Disability & Rehabilitation: Assistive Technology Vol. 17; no. 7; pp. 731 - 738
Autores principales: Khaliq Fard, Mahdie, Fallah, Ali, Maleki, Ali
Formato: meta analysis research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Oct2022
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158790476&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 158790476
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        17483107
        1X04
      jtl: Disability & Rehabilitation: Assistive Technology
      issn: 17483107
      maglogo: Y
    pubinfo:
      dt: Oct2022
      vid: 17
      iid: 7
      pid: 377
      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
    artinfo:
      ui:
        158790476
        146964838
        158790476
        158790476
        10.1080/17483107.2020.1842919
        158790476
      ppf: 731
      ppct: 7
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Neural decoding of continuous upper limb movements: a meta-analysis.
      aug:
        au:
          Khaliq Fard, Mahdie
          Fallah, Ali
          Maleki, Ali
        affil: Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
      sug:
        subj:
          Brain Mapping Methods
          Brain Physiology
          Upper Extremity Physiopathology
          Movement Physiology
          Neurosciences
          Nervous System Diseases Rehabilitation
          Rehabilitation Methods
          Human
          Meta Analysis
          Pilot Studies
          Loglinear Models
          Electroencephalography
          Pearson's Correlation Coefficient
          Imagination
          Amputees
          Activities of Daily Living
          Chi Square Test
          Confidence Intervals
      ab: EEG-based motion trajectory decoding makes a promising approach for neurotechnology which can be used for neural control of motion reconstruction and neurorehabilitation tools. However, the feasibility and validity of continuous motion decoding by non-invasive brain activity are not clear. The main aim of this study was to perform a meta-analysis across studies that examined the ability of EEG-based continuous motion decoding of upper limb movements. Pearson's correlation coefficient (CC) was used to evaluate the model performance of the studies and considered as an effect size. To estimate the overall effect size of neural decoding of motion trajectory across studies, characteristics of included studies were addressed and the random effect model was applied to the heterogeneous studies which estimated overall effect size distribution. Furthermore, the significant difference between the two subgroups of imagined and executed movements was analysed. The mean of the overall effect size was computed 0.46 across the nonhomogeneous studies. The results showed no significant difference between imagined and executed movements (Chi2=0.28, df = 1, p = 0.60). Meta-analysis results confirm that imagination like execution movements can be used for neural decoding of motion trajectory in neural motor control systems. Also, nonlinear compare with linear model statistically confirmed to be more beneficial for complex movements. Furthermore, a new approach of synergy-based motion decoding can be significantly effective to increase model performance and more research needs to evaluate this method for different levels of complexity of movements. Neural decoding methods base on EEG as a non-invasive brain activity, are more user friendly for neurorehabilitation than invasive methods that developing of it makes it more applicable for reconstructing activities of daily living. Neurotechnology for neural control of motion reconstruction, makes the rehabilitation tools to be more synchrony with human intentional movement that can be used to improve the brain neuroplastisity in stroke or other paralysed people. The feasibility and validity of imagined movements equal with executed movements show that amputee people also can benefit EEG-based motion decoding for controling rehabilitation tools just by imagination of their intentional movements. For neurorehabilitation tools, comparing the study outcomes illucidate that the approach of synergy-based motor control in brain activities concluded significantly high performance that highlighted the need it to more investigated in future research.
      pubtype: Academic Journal
      doctype:
        meta analysis
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N