Deep feature learning using target priors with applications in ECoG signal decoding for BCI

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1785-1791

  Wang, Zuoguan ^a   Lyu, Siwei ^b   Schalk, Gerwin ^c   Ji, Qiang ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b State University of New York   (United States)

^c WADSWORTH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECODING PERFORMANCE; DEEP FEATURE LEARNING; LABELED AND UNLABELED DATA; RESTRICTED BOLTZMANN MACHINE; SEMI-SUPERVISED LEARNING; STATE-OF-THE-ART ALGORITHMS; UNSUPERVISED FEATURE LEARNING; WEAKLY SUPERVISED LEARNING;

ARTIFICIAL INTELLIGENCE; BRAIN COMPUTER INTERFACE; DECODING; ELECTROPHYSIOLOGY;

SUPERVISED LEARNING;

EID: 84896060772     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle, Universit De Montral, and Montral Qubec. Greedy layer-wise training of deep networks. In NIPS. MIT Press, 2007.
2. Laurent Bougrain and Nanying Liang. Band-specific features improve Finger Flexion Prediction from ECoG. In JAICC, Parana, Argentine, 2009.
3. Mingwei Chang, Lev Ratinov, and Dan Roth. Guiding semi-supervision with constraint-driven learning. In ACL, 2007.
4. Rémi Flamary and Alain Rakotomamonjy. Decoding finger movements from ECoG signals using switching linear models. Technical report, September 2009.
5. Zachary V. Freudenburg, Nicolas F. Ramsey, Mark Wronkeiwicz, William D. Smart, Robert Pless, and Eric C. Leuthardt. Real-time naive learning of neural correlates in ECoG Electrophysiology. Int. Journal of Machine Learning and Computing, 2011.
6. Kuzman Ganchev, João Graca, Jennifer Gillenwater, and Ben Taskar. Posterior regularization for structured latent variable models. Journal of Machine Learning Research, pages 2001-2049, 2010.
7. Raia Hadsell, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, and Yann LeCun. Learning long-range vision for autonomous offroad driving. J. Field Robot., 26(2):120-144, February 2009.
8. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
9. Geoffrey Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14:2002, 2000.
10. J Kubánek, K J Miller, J G Ojemann, J R Wolpaw, and G Schalk. Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Eng, 6(6):066001-066001, Dec 2009.
11. Martin Langkvist, Lars Karlsson, and Amy Loutfi. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, 2012.
12. Quoc V. Le, Will Y. Zou, Serena Y. Yeung, and Andrew Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, pages 3361-3368, 2011.
13. Quoc Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg Corrado, Jeff Dean, and Andrew Ng. Building high-level features using large scale unsupervised learning. In ICML, 2012.
14. Yann Lecun, Lon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324, 1998.
15. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, pages 609-616, 2009.
16. Riwal Lefort, Ronan Fablet, and Jean-Marc Boucher. Weakly supervised classification of objects in images using soft random forests. In ECCV, 2010.
17. Nanying Liang and Laurent Bougrain. Decoding finger flexion using amplitude modulation from bandspecific ECoG. Neural Networks, (April):22-24, 2009.
18. Percy Liang, Michael I. Jordan, and Dan Klein. Learning from measurements in exponential families. In ICML '09, pages 641-648, NY, USA, 2009.
19. Gideon S. Mann and Andrew McCallum. Simple, robust, scalable semi-supervised learning via expectation regularization. In ICML, 2007.
20. D.J. McFarland and J.R. Wolpaw. Brain-computer interface operation of robotic and prosthetic devices. Computer, (10), oct. 2008.
21. A. Mohamed, G. Dahl, and G. Hinton. Acoustic modeling using deep belief networks. Audio, Speech, and Language Processing, IEEE Trans. on, PP(99):1, 2011.
22. Vinod Nair and Geoffrey Hinton. 3D Object Recognition with Deep Belief Nets. In NIPS, pages 1339- 1347. 2009.
23. Hoifung Poon and Pedro Domingos. Sum-product networks: A new deep architecture. In UAI, pages 337-346, 2011.
24. Salah Rifai, Yann Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller. The manifold tangent classifier. In NIPS, pages 2294-2302, 2011.
25. Robert E. Schapire, Marie Rochery, Mazin G. Rahim, and Narendra Gupta. Incorporating prior knowledge into boosting. In ICML, 2002.
26. Zuoguan Wang, Qiang Ji, Kai J. Miller, and Gerwin Schalk. Decoding finger flexion from electrocorticographic signals using a sparse gaussian process. In ICPR, pages 3756-3759, 2010.
27. Zuoguan Wang, qiang ji, Kai J Miller, and Gerwin Schalk. Prior knowledge improves decoding of finger flexion from electrocorticographic (ECoG) signals. Frontiers in Neuroscience, 5(127), 2011.
28. Zuoguan Wang, Gerwin Schalk, and Qiang Ji. Anatomically constrained decoding of finger flexion from electrocorticographic signals. NIPS, 2011.
29. Z. Wang, Aysegul Gunduz, Peter Brunner, A.L. Ritaccio, Q Ji, and Gerwin Schalk. Decoding onset and direction of movements using electrocorticographic (ECoG) signals in humans. Frontiers in Neuroengineering, 5, 2012.
30. Zuoguan Wang, Siwei Lyu, Gerwin Schalk, and Qiang Ji. Learning with target prior. In NIPS, 2012.
31. Laurenz Wiskott and Terrence J. Sejnowski. Slow feature analysis: unsupervised learning of invariances. Neural Comput., April 2002.
32. D F Wulsin, J R Gupta, R Mani, J A Blanco, and B Litt. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. Journal of Neural Engineering, 8(3):036015, 2011.
33. Xiaojin Zhu. Semi-supervised learning literature survey, 2006.