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Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals.
G. Pfurtscheller and C. Neuper, “Motor imagery and direct brain-computer communication,”
2001
Earlier work this paper cites.
F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for eeg-based brain–computer interfaces,”
2007
Earlier work this paper cites.
P. Herman, G. Prasad, T. M. McGinnity, and D. Coyle, “Comparative analysis of spectral approaches to feature extraction for eeg-based motor imagery classification,”
2008
Earlier work this paper cites.
C. S. DaSalla, H. Kambara, M. Sato, and Y. Koike, “Single-trial classification of vowel speech imagery using common spatial patterns,”
2009
Earlier work this paper cites.
C. S. DaSalla, H. Kambara, Y. Koike, and M. Sato, “Spatial filtering and single-trial classification of eeg during vowel speech imagery,” in
2009
Earlier work this paper cites.
M. D’Zmura, S. Deng, T. Lappas, S. Thorpe, and R. Srinivasan, “Toward eeg sensing of imagined speech,” in
2009
Earlier work this paper cites.
S. Machado, F. Araújo, F. Paes, B. Velasques, M. Cunha, H. Budde, L. F. Basile, R. Anghinah, O. Arias-Carrión, M. Cagy
2010
Earlier work this paper cites.
S. Deng, R. Srinivasan, T. Lappas, and M. D’Zmura, “Eeg classification of imagined syllable rhythm using hilbert spectrum methods,”
2010
Earlier work this paper cites.
K. Brigham and B. V. Kumar, “Imagined speech classification with eeg signals for silent communication: a preliminary investigation into synthetic telepathy,” in
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in
2010
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Cited alongside, same era.
L. Wang, X. Zhang, X. Zhong, and Y. Zhang, “Analysis and classification of speech imagery eeg for bci,”
2013
Cited alongside, same era.
J. Kim, S.-K. Lee, and B. Lee, “Eeg classification in a single-trial basis for vowel speech perception using multivariate empirical mode decomposition,”
2014
Cited alongside, same era.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Cited alongside, same era.
P. Sun and J. Qin, “Neural networks based eeg-speech models,”
2016
Later among the works it cites.
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio.” in
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville,
2016
Later among the works it cites.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in
2016
Later among the works it cites.
Y. R. Tabar and U. Halici, “A novel deep learning approach for classification of eeg motor imagery signals,”
2016
Later among the works it cites.
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S. Zhao and F. Rudzicz, “Classifying phonological categories in imagined and articulated speech,” in
2015
Cited alongside, same era.
T. Chen, T. He, M. Benesty
2015
Cited alongside, same era.
B. M. Idrees and O. Farooq, “Vowel classification using wavelet decomposition during speech imagery,” in
2016
Cited alongside, same era.
K. Mohanchandra and S. Saha, “A communication paradigm using subvocalized speech: translating brain signals into speech,”
2016
Cited alongside, same era.
E. F. González-Castañeda, A. A. Torres-García, C. A. Reyes-García, and L. Villaseñor-Pineda, “Sonification and textification: Proposing methods for classifying unspoken words from eeg signals,”
2017
Later among the works it cites.
2018
Later among the works it cites.
X. Zhang, L. Yao, Q. Z. Sheng, S. S. Kanhere, T. Gu, and D. Zhang, “Converting your thoughts to texts: Enabling brain typing via deep feature learning of eeg signals,” in
2018
Later among the works it cites.
P. Saha and S. Fels, “Hierarchical deep feature learning for decoding imagined speech from eeg,” to appear in
2019
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