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In this paper, we propose an end-to-end neural network (NN) based EEG-speech (NES) modeling framework, in which three network structures are developed to map imagined EEG signals to phonemes.
P. Suppes, Z.-L. Lu, and B. Han, “Brain wave recognition of words,” Proceedings of the National Academy of Sciences , vol. 94, no. 26, pp. 14 965–14 969, 1997
1997
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,” Journal of neural engineering , vol. 4, no. 2, p. R1, 2007
2007
Earlier work this paper cites.
A. Mnih and G. Hinton, “Three new graphical models for statistical language modelling,” in Proceedings of the 24th international conference on Machine learning . ACM, 2007, pp. 641–648
2007
Earlier work this paper cites.
T. Toda, A. W. Black, and K. Tokuda, “Statistical mapping between articulatory movements and acoustic spectrum using a gaussian mixture model,” Speech Communication , vol. 50, no. 3, pp. 215–227, 2008
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,” Neural Networks , vol. 22, no. 9, pp. 1334–1339, 2009
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 International Conference on Human-Computer Interaction . Springer, 2009, pp. 40–48
2009
Earlier work this paper cites.
A. Porbadnigk, M. Wester, and T. S. Jan-p Calliess, “Eeg-based speech recognition impact of temporal effects,” 2009
2009
Earlier work this paper cites.
R. Memisevic and G. E. Hinton, “Learning to represent spatial transformations with factored higher-order boltzmann machines,” Neural Computation , vol. 22, no. 6, pp. 1473–1492, 2010
2010
Earlier work this paper cites.
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in Proceedings of the 28th international conference on machine learning (ICML-11) , 2011, pp. 689–696
2011
Earlier work this paper cites.
M. Lopez-Gordo, E. Fernandez, S. Romero, F. Pelayo, and A. Prieto, “An auditory brain–computer interface evoked by natural speech,” Journal of neural engineering , vol. 9, no. 3, p. 036013, 2012
2012
Cited alongside, same era.
L. Hausfeld, F. De Martino, M. Bonte, and E. Formisano, “Pattern analysis of eeg responses to speech and voice: Influence of feature grouping,” Neuroimage , vol. 59, no. 4, pp. 3641–3651, 2012
2012
Cited alongside, same era.
N. Srivastava and R. R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” in Advances in neural information processing systems , 2012, pp. 2222–2230
2012
Cited alongside, same era.
L. Wang, X. Zhang, X. Zhong, and Y. Zhang, “Analysis and classification of speech imagery eeg for bci,” Biomedical Signal Processing and Control , vol. 8, no. 6, pp. 901–908, 2013
2013
Cited alongside, same era.
R. Kiros, R. Salakhutdinov, and R. S. Zemel, “Multimodal neural language models.” in ICML , vol. 14, 2014, pp. 595–603
2014
Later among the works it cites.
T. Yamashita, M. Tanaka, E. Yoshida, Y. Yamauchi, and H. Fujiyoshi, “To be bernoulli or to be gaussian, for a restricted boltzmann machine.” in ICPR , 2014, pp. 1520–1525
2014
Later among the works it cites.
G. M. Di Liberto, J. A. O’Sullivan, and E. C. Lalor, “Low-frequency cortical entrainment to speech reflects phoneme-level processing,” Current Biology , vol. 25, no. 19, pp. 2457–2465, 2015
2015
Later among the works it cites.
S. Zhao and F. Rudzicz, “Classifying phonological categories in imagined and articulated speech,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 992–996
2015
Later among the works it cites.
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A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, T. Mikolov et al. , “Devise: A deep visual-semantic embedding model,” in Advances in neural information processing systems , 2013, pp. 2121–2129
2013
Cited alongside, same era.
2013
Cited alongside, same era.
S. Gao, Y. Wang, X. Gao, and B. Hong, “Visual and auditory brain–computer interfaces,” IEEE Transactions on Biomedical Engineering , vol. 61, no. 5, pp. 1436–1447, 2014
2014
Cited alongside, same era.
M. Matsumoto and J. Hori, “Classification of silent speech using support vector machine and relevance vector machine,” Applied Soft Computing , vol. 20, pp. 95–102, 2014
2014
Cited alongside, same era.
R. Socher, A. Karpathy, Q. V. Le, C. D. Manning, and A. Y. Ng, “Grounded compositional semantics for finding and describing images with sentences,” Transactions of the Association for Computational Linguistics , vol. 2, pp. 207–218, 2014
2014
Cited alongside, same era.
J. A. O’Sullivan, A. J. Power, N. Mesgarani, S. Rajaram, J. J. Foxe, B. G. Shinn-Cunningham, M. Slaney, S. A. Shamma, and E. C. Lalor, “Attentional selection in a cocktail party environment can be decoded from single-trial eeg,” Cerebral Cortex , vol. 25, no. 7, pp. 1697–1706, 2015
2015
Later among the works it cites.
C. Herff and T. Schultz, “Automatic speech recognition from neural signals: A focused review,” Frontiers in Neuroscience , vol. 10, 2016
2016
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2016
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N. Yoshimura, A. Nishimoto, A. N. Belkacem, D. Shin, H. Kambara, T. Hanakawa, and Y. Koike, “Decoding of covert vowel articulation using electroencephalography cortical currents,” Frontiers in neuroscience , vol. 10, 2016
2016
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