@InProceedings{Carvalho:2017:DeLeAp,
               author = "Carvalho, Schubert R",
          affiliation = "{Instituto Tecnol{\'o}gico Vale}",
                title = "A Deep Learning Approach for Classification of Reaching Targets 
                         from EEG Images",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Deep Learning, EEG, BCI, Reaching Targets.",
             abstract = "In this paper, we propose a new approach for the classification of 
                         reaching targets before movement onset, during visually-guided 
                         reaching in 3D space. Our approach combines the discriminant power 
                         of two-dimensional Electroencephalography (EEG) signals (i.e., EEG 
                         images) built from short epochs, with the feature extraction and 
                         classification capabilities of deep learning (DL) techniques, such 
                         as the Convolutional Neural Networks (CNN). In this work, reaching 
                         motions are performed into four directions: left, right, up and 
                         down. To allow more natural reaching movements, we explore the use 
                         of Virtual Reality (VR) to build an experimental setup that allows 
                         the subject to perform self-paced reaching in 3D space while 
                         standing. Our results reported an increase both in classification 
                         performance and early detection in the majority of our 
                         experiments. To our knowledge this is the first time that EEG 
                         images and CNN are combined for the classification of reaching 
                         targets before movement onset.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.30",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.30",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PMKTFL",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PMKTFL",
           targetfile = "PID4959895.pdf",
        urlaccessdate = "2024, Apr. 29"
}