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In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, Physiotoolkit, and Physionet,” Circulation , vol. 101, no. 23, pp. e215–e220, 2000
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J. W. Peirce, “PsychoPy—psychophysics Software in Python,” Journal of neuroscience methods , vol. 162, no. 1-2, pp. 8–13, 2007
2007
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G. Gargiulo, R. A. Calvo, P. Bifulco, M. Cesarelli, C. Jin, A. Mohamed, and A. van Schaik, “A New EEG Recording System for Passive Dry Electrodes,” Clinical Neurophysiology , vol. 121, no. 5, pp. 686–693, 2010
2010
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G. Edlinger and C. Guger, “Can Dry EEG Sensors Improve the Usability of SMR, P300 and SSVEP Based BCIs?” in Towards Practical Brain-Computer Interfaces . Springer, 2012, pp. 281–300
2012
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R. P. Rao, Brain-Computer Interfacing: an Introduction . New York, NY, USA: Cambridge University Press, 2013
2013
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T. Mullen, C. Kothe, Y. M. Chi, A. Ojeda, T. Kerth, S. Makeig, G. Cauwenberghs, and T.-P. Jung, “Real-time Modeling and 3D Visualization of Source Dynamics and Connectivity using Wearable EEG,” in International Conference of Engineering in Medicine and Biology Society . IEEE, 2013, pp. 2184–2187
2013
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A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Classification of Covariance Matrices using a Riemannian-based Kernel for BCI Applications,” Neurocomputing , vol. 112, pp. 172–178, 2013
2013
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M. A. Lopez-Gordo, D. Sanchez-Morillo, and F. Pelayo Valle, “Dry EEG electrodes,” Sensors , vol. 14, no. 7, pp. 12 847–12 870, 2014
2014
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Y.-P. Lin, Y. Wang, C.-S. Wei, and T.-P. Jung, “Assessing the Quality of Steady-State Visual-Evoked Potentials for Moving Humans using a Mobile Electroencephalogram Headset,” Frontiers in Human Neuroscience , vol. 8, no. March, pp. 1–10, 2014
2014
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2014
Cited alongside, same era.
T. R. Mullen, C. A. Kothe, Y. M. Chi, A. Ojeda, T. Kerth, S. Makeig, T.-P. Jung, and G. Cauwenberghs, “Real-time Neuroimaging and Cognitive Monitoring using Wearable Dry EEG,” IEEE Transactions on Biomedical Engineering , vol. 62, no. 11, pp. 2553–2567, 2015
2015
Cited alongside, same era.
D. E. Callan, G. Durantin, and C. Terzibas, “Classification of Single-trial Auditory Events using Dry-wireless EEG during Real and Motion Simulated Flight,” Frontiers in Systems Neuroscience , vol. 9, no. February, pp. 1–12, 2015
2015
Cited alongside, same era.
A. M. Norcia, L. G. Appelbaum, J. M. Ales, B. R. Cottereau, and B. Rossion, “The Steady-state Visual Evoked Potential in Vision Research: A Review,” Journal of vision , vol. 15, no. 6, pp. 4–4, 2015
2015
Cited alongside, same era.
2016
Later among the works it cites.
J. Minguillon, M. A. Lopez-Gordo, and F. Pelayo, “Trends in EEG-BCI for Daily-life: Requirements for Artifact Removal,” Biomedical Signal Processing and Control , vol. 31, pp. 407–418, 2017
2017
Later among the works it cites.
N. S. Kwak, K. R. Müller, and S. W. Lee, “A Convolutional Neural Network for Steady State Visual Evoked Potential Classification under Ambulatory Environment,” PLoS ONE , vol. 12, no. 2, pp. 1–20, 2017
2017
Later among the works it cites.
R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, “Deep learning with Convolutional Neural Networks for EEG Decoding and Visualization,” Human brain mapping , vol. 38, no. 11, pp. 5391–5420, 2017
2017
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Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2016
Cited alongside, same era.
G. Lisi, M. Hamaya, T. Noda, and J. Morimoto, “Dry-wireless EEG and Asynchronous Adaptive Feature Extraction Towards a Plug-and-play Co-adaptive Brain Robot Interface,” in IEEE International Conference on Robotics and Automation . IEEE, 2016, pp. 959–966
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016
2016
Cited alongside, same era.
Later among the works it cites.
J. Thomas, T. Maszczyk, N. Sinha, T. Kluge, and J. Dauwels, “Deep learning-based classification for brain-computer interfaces,” in IEEE International Conference on Systems, Man, and Cybernetics . IEEE, 2017, pp. 234–239
2017
Later among the works it cites.
2018
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F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo, A. Rakotomamonjy, and F. Yger, “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update,” Journal of neural engineering , 2018
2018
Closest in time.