Dataset : Review SVM EEG

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Collection

Quotation

Coralie Joucla (2022): Review SVM EEG. Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive. doi:10.25666/DATAOSU-2022-03-25

General metadata

Identifier : local : FR-18008901306731-2022-03-25 external : doi:10.25666/DATAOSU-2022-03-25
Description :
Dataset (table) of a literature review of Electroencephalography (EEG) and Support Vector Machines (SVM), up to and including 2020.
Disciplines :
computer science, artificial intelligence (engineering science), computer science, software engineering (engineering science), neuroimaging (fundamental biology), neurosciences (fundamental biology), physiology (fundamental biology), psychology (fundamental biology), statistics & probability (mathematics), clinical neurology (medical research), psychiatry (medical research, humanities)
Keywords :

Dates :
Data acquisition : from 22 May 2020 to 14 Jan 2021
Data provision : 22 Mar 2022
Metadata record :
Creation : 25 Mar 2022

Update periodicity : no update
Language : English (eng)
Additional information :
Data collected in the framework of Coralie Joucla's PhD in Neuroscience, directed by Pr. Emmanuel Haffen (Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive, Université de Bourgogne - Franche-Comté, Besançon, France ; Psychiatrie clinique, Hôpital Universitaire CHRU, Besançon, France), co-supervised by Dr. Damien Gabriel (Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive, Université Bourgogne - Franche-Comté, Besançon, France) and Dr. Juan-Pablo Ortega (Math Department, University of Sankt-Gallen, Sankt-Gallen, Switzerland).
Audience : University: master, Research

Coverages

Time coverage :

Administrative metadata

Data creatorAffiliation
Coralie JouclaUR-LINC (FRA)
Publisher : Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive
Science contact : Coralie Joucla e-mail
Project and funder :
Access : available

Technical metadata

Formats : application/pdf, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, text/csv
Data acquisition methods :
  • Derived or compiled data :
    The PubMed database was queried to collect a list of publications, on 22 May 2020, and on 14 January 2021, to complete the year 2020. Not all fields were collected in this second round.

    The research equation used was: ``eeg support vector machine classification NOT epilepsy NOT seizure NOT review NOT sleep'', thus translated into Mesh keywords:
    ((``electroencephalography''[MeSH Terms] OR ``electroencephalography''[All Fields] OR ``eeg''[All Fields])
    AND (``support vector machine''[MeSH Terms]
    OR (``support''[All Fields] AND ``vector''[All Fields] AND ``machine''[All Fields])
    OR ``support vector machine''[All Fields])
    AND (``classification''[Subheading] OR ``classification''[All Fields] OR ``classification''[MeSH Terms]))
    NOT (``epilepsy''[MeSH Terms] OR ``epilepsy''[All Fields])
    NOT (``seizures'' [MeSH Terms] OR ``seizures'' [All Fields] OR ``seizure'' [All Fields])
    NOT (``review''[Publication Type] OR ``review literature as topic''[MeSH Terms] OR ``review''[All Fields])
    NOT (``sleep''[MeSH Terms] OR ``sleep''[All Fields]).


    Inclusion criteria:
    - Studies on the SVM classification of EEG signals.
    - Studies on human subjects only.

    Exclusion criteria:
    - Studies involving electrocorticography (ECoG) and intracortical EEG, or magnetoencephalography (MEG).
    - Literature reviews and meta-analyses.
    - Articles in languages other than English.
    - Studies on EEG and SVM, but not in classification.

    Description of the fields presented in the attached PDF.
Datatype : Dataset

Publications

  • Électroencéphalographie et machines à vecteurs de support dans le diagnostic différentiel des pathologies neuropsychiatriques : état des lieux, enjeux et applications (chapitre 3). (hal:tel-03353516)
  • Three simple steps to improve the interpretability of EEG-SVM studies (doi:10.1101/2021.12.14.472588)
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