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@ARTICLE{Hasasneh:845485,
      author       = {Hasasneh, Ahmad and Kampel, Nikolas and Sripad, Praveen and
                      Shah, N. J. and Dammers, Jürgen},
      title        = {{D}eep {L}earning {A}pproach for {A}utomatic
                      {C}lassification of {O}cular and {C}ardiac {A}rtifacts in
                      {MEG} {D}ata},
      journal      = {Journal of Engineering},
      volume       = {2018},
      issn         = {2314-4912},
      address      = {New York, NY},
      publisher    = {Hindawi Publishing},
      reportid     = {FZJ-2018-02719},
      pages        = {1 - 10},
      year         = {2018},
      abstract     = {We propose an artifact classification scheme based on a
                      combined deep and convolutional neural network (DCNN) model,
                      to automatically identify cardiac and ocular artifacts from
                      neuromagnetic data, without the need for additional
                      electrocardiogram (ECG) and electrooculogram (EOG)
                      recordings. From independent components, the model uses both
                      the spatial and temporal information of the decomposed
                      magnetoencephalography (MEG) data. In total, 7122 samples
                      were used after data augmentation, in which task and nontask
                      related MEG recordings from 48 subjects served as the
                      database for this study. Artifact rejection was applied
                      using the combined model, which achieved a sensitivity and
                      specificity of $91.8\%$ and $97.4\%,$ respectively. The
                      overall accuracy of the model was validated using a
                      cross-validation test and revealed a median accuracy of
                      $94.4\%,$ indicating high reliability of the DCNN-based
                      artifact removal in task and nontask related MEG
                      experiments. The major advantages of the proposed method are
                      as follows: (1) it is a fully automated and user independent
                      workflow of artifact classification in MEG data; (2) once
                      the model is trained there is no need for auxiliary signal
                      recordings; (3) the flexibility in the model design and
                      training allows for various modalities (MEG/EEG) and various
                      sensor types.},
      cin          = {INM-4 / JARA-BRAIN},
      ddc          = {620},
      cid          = {I:(DE-Juel1)INM-4-20090406 / $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000432190600001},
      doi          = {10.1155/2018/1350692},
      url          = {https://juser.fz-juelich.de/record/845485},
}