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Bigdely-Shamlo, A. Vankov, R.R. Ramirez, and S. Makeig, \u201cBrain activity-based image classification from rapid serial visual presentation,\u201d IEEE Trans. Neural Syst. Rehabil. Eng., vol.16, no.5, pp.432-441, 2008. 10.1109\/tnsre.2008.2003381","DOI":"10.1109\/TNSRE.2008.2003381"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S.-W. Chuang, L.-W. Ko, Y.-P. Lin, R.-S. Huang, T.-P. Jung, andC.-T. Lin, \u201cCo-modulatory spectral changes in independent brain processes are correlated with task performance,\u201d Neuroimage, vol.62, no.3, pp.1469-1477, 2012. 10.1016\/j.neuroimage.2012.05.035","DOI":"10.1016\/j.neuroimage.2012.05.035"},{"key":"3","unstructured":"[3] B. Scholkopft and K.R. Mullert, \u201cFisher discriminant analysis with kernels,\u201d Neural Networks for Signal Processing IX 1, 1999."},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] U. Hoffmann, J.-M. Vesin, T. Ebrahimi, and K. Diserens, \u201cAn efficient p300-based brain-computer interface for disabled subjects,\u201d Journal of Neuroscience methods, vol.167, no.1, pp.115-125, 2008. 10.1016\/j.jneumeth.2007.03.005","DOI":"10.1016\/j.jneumeth.2007.03.005"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] C. Cortes and V. Vapnik, \u201cSupport-vector networks,\u201d Machine learning, vol.20, no.3, pp.273-297, 1995. 10.1007\/bf00994018","DOI":"10.1007\/BF00994018"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] J. Meng, L.M. Meri\u00f1o, K. Robbins, and Y. Huang, \u201cClassification of imperfectly time-locked image rsvp events with eeg device,\u201d Neuroinformatics, vol.12, no.2, pp.261-275, 2014. 10.1007\/s12021-013-9203-4","DOI":"10.1007\/s12021-013-9203-4"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] J. Meng, L.M. Meri\u00f1o, N.B. Shamlo, S. Makeig, K. Robbins, and Y. Huang, \u201cCharacterization and robust classification of eeg signal from image rsvp events with independent time-frequency features,\u201d PloS one, vol.7, e44464.2, 2012. 10.1371\/journal.pone.0044464","DOI":"10.1371\/journal.pone.0044464"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, \u201cxdawn algorithm to enhance evoked potentials: application to brain-computer interface,\u201d IEEE Trans. Biomed. Eng., vol.56, no.8, pp.2035-2043, 2009. 10.1109\/tbme.2009.2012869","DOI":"10.1109\/TBME.2009.2012869"},{"key":"9","unstructured":"[9] H. Cecotti and A. Gr\u00e4ser, \u201cNeural network pruning for feature selection-application to a p300 brain-computer interface,\u201d ESANN, Citeseer, 2009."},{"key":"10","unstructured":"[10] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Advances in Neural Information Processing Systems, pp.1097-1105, 2012."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Graves, A.-R. Mohamed, and G. Hinton, \u201cSpeech recognition with deep recurrent neural networks,\u201d 2013 IEEE international conference on acoustics, speech and signal processing, IEEE, pp.6645-6649, 2013. 10.1109\/icassp.2013.6638947","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"12","unstructured":"[12] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, \u201cLarge-scale video classification with convolutional neural networks,\u201d Proc. IEEE conference on Computer Vision and Pattern Recognition, pp.1725-1732, 2014."},{"key":"13","unstructured":"[13] X. Zhang and Y. LeCun, \u201cText understanding from scratch,\u201d arXiv preprint arXiv:1502.01710, 2015."},{"key":"14","unstructured":"[14] K.M. Hermann, T. Kocisky, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom, \u201cTeaching machines to read and comprehend,\u201d Advances in Neural Information Processing Systems, pp.1693-1701, 2015."},{"key":"15","unstructured":"[15] J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, \u201cBeyond short snippets: Deep networks for video classification,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.4694-4702, 2015."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] P.W. Mirowski, Y. LeCun, D. Madhavan, and R. Kuzniecky, \u201cComparing svm and convolutional networks for epileptic seizure prediction from intracranial eeg,\u201d 2008 IEEE Workshop on Machine Learning for Signal Processing, IEEE, pp.244-249, 2008. 10.1109\/mlsp.2008.4685487","DOI":"10.1109\/MLSP.2008.4685487"},{"key":"17","unstructured":"[17] P. Bashivan, I. Rish, M. Yeasin, and N. 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Giesbrecht, \u201cSingle-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.25, no.11, pp.2030-2042, 2014. 10.1109\/tnnls.2014.2302898","DOI":"10.1109\/TNNLS.2014.2302898"},{"key":"21","unstructured":"[21] S. Stober, D.J. Cameron, and J.A. Grahn, \u201cUsing convolutional neural networks to recognize rhythm? stimuli from electroencephalography recordings,\u201d Advances in Neural Information Processing Systems, pp.1449-1457, 2014."},{"key":"22","unstructured":"[22] S. Stober, D.J. Cameron, and J.A. Grahn, \u201cClassifying eeg recordings of rhythm perception,\u201d ISMIR, pp.649-654, 2014."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] S. Ahmed, L.M. Merino, Z. Mao, J. Meng, K. Robbins, and Y. Huang, \u201cA deep learning method for classification of images rsvp events with eeg data,\u201d Global Conference on Signal and Information Processing (Global SIP), 2013 IEEE, IEEE, pp.33-36, 2013. 10.1109\/globalsip.2013.6736804","DOI":"10.1109\/GlobalSIP.2013.6736804"},{"key":"24","unstructured":"[24] M. Hajinoroozi, Z. Mao, and Y. Huang, \u201cDeep transfer learning for cross-experiment prediction of rapid serial visual presentation events,\u201d 2013 IEEE Brain Computer Interface Meeting, pp.55-60, 2013."},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Y. Bengio, \u201cLearning deep architectures for ai,\u201d Foundations and trends\u00ae in Machine Learning, vol.2, no.1, pp.1-127, 2009. 10.1561\/2200000006","DOI":"10.1561\/2200000006"},{"key":"26","unstructured":"[26] J. Yosinski, J. Clune, Y. Bengio, and H. 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Hanna, C. Bahlmann, M.K. Singh, and S.-F. Chang, \u201cIn a blink of an eye and a switch of a transistor: cortically coupled computer vision,\u201d Proc. IEEE, vol.98, no.3, pp.462-478, 2010. 10.1109\/jproc.2009.2038406","DOI":"10.1109\/JPROC.2009.2038406"},{"key":"33","unstructured":"[33] S. Kunwar, \u201cSubject independent p300 erp detection and classification,\u201d NER2015, 2015."},{"key":"34","unstructured":"[34] T. Lan, \u201cFeature extraction feature selection and dimensionality reduction techniques for brain computer interface,\u201d OHSU Digital Collections, https:\/\/doi.org\/10.6083\/M4RX9920, 2011."},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] P. Sajda, A.D. Gerson, M.G. Philiastides, and L.C. 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