Journal of Neural Engineering - Journal - Bridge of Knowledge

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Journal of Neural Engineering

ISSN:

1741-2560

eISSN:

1741-2552

Disciplines
(Field of Science):

  • information and communication technology (Engineering and Technology)
  • biomedical engineering (Engineering and Technology)
  • materials engineering (Engineering and Technology)
  • medical biology (Medical and Health Sciences )
  • pharmacology and pharmacy (Medical and Health Sciences )
  • medical sciences (Medical and Health Sciences )
  • biotechnology (Natural sciences)
  • biological sciences (Natural sciences)

Ministry points: Help

Ministry points - current year
Year Points List
Year 2024 140 Ministry scored journals list 2024
Ministry points - previous years
Year Points List
2024 140 Ministry scored journals list 2024
2023 140 Ministry Scored Journals List
2022 140 Ministry Scored Journals List 2019-2022
2021 140 Ministry Scored Journals List 2019-2022
2020 140 Ministry Scored Journals List 2019-2022
2019 140 Ministry Scored Journals List 2019-2022
2018 35 A
2017 35 A
2016 35 A
2015 35 A
2014 35 A
2013 35 A
2012 25 A
2011 25 A
2010 32 A

Model:

Hybrid

Points CiteScore:

Points CiteScore - current year
Year Points
Year 2023 7.8
Points CiteScore - previous years
Year Points
2023 7.8
2022 7.5
2021 8.4
2020 7.7
2019 7.6
2018 6.9
2017 6.9
2016 7.2
2015 7.4
2014 7.8
2013 7.1
2012 6.4
2011 5.1

Impact Factor:

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Sherpa Romeo:

https://v2.sherpa.ac.uk/id/publication/11322

Papers published in journal

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total: 2

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Catalog Journals

Year 2023
  • Rating by detection: an artifact detection protocol for rating EEG quality with average event duration
    Publication
    • D. Węsierski
    • M. R. Rufuie
    • O. Milczarek
    • W. Ziembla
    • P. Ogniewski
    • A. Kołodziejak
    • P. Niedbalski

    - Journal of Neural Engineering - Year 2023

    Quantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real EEG optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically...

    Full text available to download

Year 2017
  • Behavioral state classification in epileptic brain using intracranial electrophysiology
    Publication
    • V. Kremen
    • J. J. Duque
    • B. Brinkmann
    • B. M. Berry
    • M. T. Kucewicz
    • F. Khadjevand
    • J. Van Gompel
    • M. Stead
    • E. K. ST.Louis
    • G. A. Worrell

    - Journal of Neural Engineering - Year 2017

    OBJECTIVE: Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode...

    Full text to download in external service

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