A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2014, Pages 1746-1753

  Sun, Shiliang ^a   Zhou, Jin ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Adaptive Classification; Adaptive Feature Extraction; Brain Computer Interface; Electroencephalogram; Machine Learning

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; BRAIN; CLASSIFICATION (OF INFORMATION); COMPUTER CONTROL SYSTEMS; ELECTROENCEPHALOGRAPHY; EXTRACTION; FEATURE EXTRACTION; LEARNING SYSTEMS; MACHINE LEARNING; NEUROPHYSIOLOGY;

ADAPTIVE CLASSIFICATION; ADAPTIVE FEATURE EXTRACTIONS; ADAPTIVE LEARNING; BRAIN COMPUTER INTERACTIONS; DYNAMIC VARIATIONS; EFFECTIVE APPROACHES; ELECTRICAL SIGNAL; ELECTROENCEPHALOGRAM SIGNALS;

BRAIN COMPUTER INTERFACE;

EID: 84908495185     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889525     Document Type: Conference Paper

References (68)

1. O. AlZoubi, R. A. Calvo, and R. H. Stevens, "Classification of EEG for affect recognition: an adaptive approach", Lecture Notes in Computer Science, vol. 5866, pp. 52-61, 2009.
2. C. W. Anderson, and Z. Sijercic, "Classification of EEG signals from four subjects during five mental tasks", Proceedings of the Conference on Engineering Applications in Neural Networks, pp. 407-414, 1996.
3. M. Arnold, X. H. R. Milner, H. Witte, R. Bauer, and C. Braun, "Adaptive AR modeling of nonstationary time series by means of Kalman filtering", IEEE Transactions on Biomedical Engineering, vol. 45, pp. 553-562, 1998.
4. A. B. Barreto, A. M. Taberner, and L. M. Vicente, "Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface", Proceedings of the IEEE SouthEastcon Conference, pp. 99-102, 1996.
5. G. Baudat, and F. Anouar, "Generalized discriminant analysis using a kernel approach", Neural computation, vol. 12, pp. 2385-2404, 2000.
6. K. P. Bennett, and A. Demiriz, "Semi-supervised support vector machines", Proceedings of Neural Information Processing Systems, pp. 368-374, 1998.
7. J. Blumberg, J. Rickert, S. Waldert, A. Schulze-Bonhage, A. Aertsen, and C. Mehring, "Adaptive classification for brain computer interfaces", Proceedings of the 29th annual international conference of the IEEE EMBS, pp. 2536-2539, 2007.
8. C. J. Burges, "A tutorial on support vector machines for pattern recognition", Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
9. C. Chen, W. Song, J. Zhang, Z. Hu, and H. Xu, "An Adaptive Feature Extraction Method for Motor-Imagery BCI Systems", Proceedings of the International Conference on Computational Intelligence and Security, pp. 275-279, 2010.
10. R. Coifman, and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection", IEEE Transactions on Information Theory, vol. 38, pp. 713-718, 1992.
11. E. A. Curran, and M. J. Stokes, "Learning to control brain activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems", Brain and Cognition, vol. 51, pp. 326-336, 2003.
12. G. Dornhege, B. Blankertz, and G. Curio, "Speeding up classification of multi-channel brain-computer interfaces: Common spatial patterns for slow cortical potentials", Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, pp. 595-598, 2003.
13. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, second edition, John Wiley & Sons, 2000.
14. E. Haselsteiner, and G. Pfurtscheller, "Using time-dependent neural networks for EEG classification", IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 457-463, 2000.
15. M. K. Hazrati, and A. Erfanian, "An online EEG-based brain C computer interface for controlling hand grasp using an adaptive probabilistic neural network", Medical Engineering & Physics, vol. 32, pp. 730-739, 2010.
16. A. Hiraiwa, K. Shimohara, and Y. Tokunaga, "EEG topography recognition by neural networks", Engineering in Medicine and Biology Magazine, vol. 9, pp. 39-42, 1990.
17. B. H. Jansen, J. R. Bourne, and J. W. Ward, "Autoregressive estimation of short segment spectra for computerized EEG analysis", IEEE Transactions on Biomedical Engineering, vol. 9, pp. 630-638, 1981.
18. A. Kostov, and M. Polak, "Parallel man-machine training in development of EEG-based cursor control", IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 203-205, 2000.
19. M. Krauledat, M. Tangermann, B. Blankertz, and K. R. Müller, "Towards zero training for brain-computer interfacing", PLoS One, vol. 3, pp. 2967, 2008.
20. H. Lee, J. Yoo, and S. Choi, "Semi-supervised nonnegative matrix factorization", Signal Processing Letters, vol. 17, pp. 4-7, 2010.
21. Y. Li, and C. Guan, "Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm", Machine Learning, vol. 71, pp. 33-53, 2008.
22. G. Liu, G. Huang, J. Meng, D. Zhang, and X. Zhu, "Improved GMM with parameter initialization for unsupervised adaptation of braincomputer interface", International Journal for Numerical Methods in Biomedical Engineering, vol. 26, pp. 681-691, 2010.
23. F. Lotte, M. Congedo, A. Lcuyer, F. Lamarche, and B. Arnaldi, "A review of classification algorithms for EEG-based brain-computer interfaces", Journal of Neural Engineering, vol. 4, pp. 1-13, 2007.
24. J. Millan, "On the need for on-line learning in brain-computer interfaces", Proceedings of the International Joint Conference on Neural Networks, pp. 2877-2882, 2004.
25. J. R. Millan, F. Renkens, J. Mouriño, and W. Gerstner, "Noninvasive brain-actuated control of a mobile robot by human EEG", IEEE Transactions on Biomedical Engineering, vol. 51, pp. 1026-1033, 2004.
26. J. R. Millan, F. Renkens, J. Mouriño, and W. Gerstner, "Brain-actuated interaction", Artificial Intelligence, vol. 159, pp. 241-259, 2004.
27. J. Müller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, "Designing optimal spatial filters for single-trial EEG classification in a movement task", Clinical Neurophysiology, vol. 110, pp. 787-798, 1999.
28. M. A. L. Nicolelis, "Actions from thoughts", Nature, vol. 409, pp. 403-407, 2001.
29. M. A. Oskoei, J. Q. Gan, and H. Hu, "Adaptive schemes applied to online SVM for BCI data classification", Proceedings of 31st Annual International Conference of the IEEE EMBS, pp. 2600-2603, 2009.
30. M. A. Oskoei, and H. Hu, "Support vector machine-based classification scheme for myoelectric control applied to upper limb", IEEE Transactions on Biomedical Engineering, vol. 55, pp. 1956-1965, 2008.
31. S. J. Pan, and Q. Yang, "A survey on transfer learning", IEEE Transactions on Knowledge and Data Engineering, vol. 22, pp. 1345-1359, 2010.
32. L. Parra, C. Alvino, A. C. Tang, B. A. Pearlmutter, N. Yeung, A. Osman, and P. Sajda, "Linear spatial integration for single trial detection in encephalography", NeuroImage, vol. 7, pp. 223-230, 2002.
33. G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlögl, B. Obermaier, and M. Pregenzer, "Current trends in Graz brain-computer interface (BCI) research", IEEE Transactions on Rehabilitation Engineering, vol. 8, pp. 216-219, 2000.
34. G. Pfurtscheller, C. Neuper, A. Schlogl, and K. Lugger, "Sparability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters", IEEE Transactions on Rehabilitation Engineering, vol. 6, pp. 316-325, 1998.
35. J. Qin, and Y. Li, "An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems", Proceedings of 18th International Conference on Pattern Recognition, pp. 1240-1243, 2006.
36. L. Rutkowski, "Adaptive probabilistic neural networks for pattern classification in time-varying environment", IEEE Transactions on Neural Networks, vol. 15, pp. 811-827, 2004.
37. A. Schlögl, D. Flotzinger, and G. Pfurtscheller, "Adaptive Autoregressive Modeling used for Single-trial EEG Classification-Verwendung eines Adaptiven Autoregressiven Modells für die Klassifikation von Einzeltrial-EEG-Daten", Biomedizinische Technik, vol. 42, pp. 162-167, 1997.
38. A. Schlögl, K. Lugger, and G. Pfurtscheller, "Using adaptive autoregressive parameters for a brain-computer-interface experiment", Proceedings of the 19th Annual International Conference on Medicine and Biology Society, pp. 1533-1535, 1997.
39. A. Schlogl, S. J. Roberts, and G. Pfurtscheller", A criterion for adaptive autoregressive models", Proceedings of the IEEE International Conference on Engineering in Medicine and Biology Society, pp. 1581-1582, 2000.
40. D. F. Specht, "Probabilistic neural networks", Neural networks, vol. 3, pp. 109-118, 1990.
41. D. F. Specht, "A general regression neural network", IEEE Transactions on Neural Networks, vol. 2, pp. 568-576, 1991.
42. S. Sun, "The extreme energy ratio criterion for EEG feature extraction", Lecture Notes in Computer Science, vol. 5164, pp. 919-928, 2008.
43. S. Sun, "Extreme energy difference for feature extraction of EEG signals", Expert Systems with Applications, vol. 37, pp. 4350-4357, 2010.
44. S. Sun, "Local within-class accuracies for weighting individual outputs in multiple classifier systems", Pattern Recognition Letters, vol. 31, pp. 119-124, 2010.
45. S. Sun, Y. Lu, Y. Chen, "The stochastic approximation method for adaptive Bayesian classifiers: Towards online brain-computer interfaces", Neural Computing and Applications, vol. 20, pp. 31-40, 2011.
46. S. Sun, Z. Xu, and M. Yang, "Transfer learning with part-based ensembles", Lecture Notes in Computer Science, vol. 7872, pp. 271-282, 2013.
47. S. Sun, and C. Zhang, "Learning on-line classification via decorrelated LMS algorithm: application to brain-computer interfaces", Proceedings of 8th International Conference on Discovery Science, pp. 215-226, 2005.
48. S. Sun, and C. Zhang, "Adaptive feature extraction for EEG signal classification", Medical and Biological Engineering and Computing, vol. 44, pp. 931-935, 2006.
49. S. Sun, and C. Zhang, "Subspace ensembles for classification", Physica A: Statistical Mechanics and Its Applications, vol. 385, pp. 199-207, 2007.
50. S. Sun, and Q. Zhang, "Multiple-view multiple-learner semisupervised learning", Neural Processing Letters, vol. 34, pp. 229-240, 2011.
51. S. Sun, C. Zhang, and D. Zhang, "An experimental evaluation of ensemble methods for EEG signal classification", Pattern Recognition Letters, vol. 28, pp. 2157-2163, 2007.
52. P. Sykacek, S. J. Roberts, and M. Stokes, "Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation", IEEE Transactions on Biomedical Engineering, vol. 51, pp. 719-727, 2004.
53. W. Tu, and S. Sun, "Semi-supervised feature extraction with local temporal regularization for EEG Classification", Proceedings of the International Joint Conference on Neural Networks, pp. 75-80, 2011.
54. W. Tu, and S. Sun, "Semi-supervised feature extraction for EEG classification", Pattern Analysis and Applications, vol. 16, pp. 213-222, 2012.
55. W. Tu, and S. Sun, "A subject transfer framework for EEG classification", Neurocomputing, vol. 82, pp. 109-116, 2012.
56. W. Tu, S. Sun, "Dynamical ensemble learning with model friendly classifiers for domain adaptation", Proceedings of the 21st International Conference on Pattern Recognition, pp. 1181-1184, 2012.
57. T. M. Vaughan, W. J. Heetderks, L. J. Trejo, W. Z. Rymer, M. Weinrich, M. M. Moore, A. Kübler, B. H. Dobkin, N. Birbaumer, E. Donchin, E. W. Wolpaw, and J. R. Wolpaw, "Guest editorial braincomputer interface technology: brain-computer interface technology: a review of the second international meeting", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 94-109, 2003.
58. C. Vidaurre, M. Kawanabe, P. von Bunau, B. Blankertz, and K. R. Muller, "Toward unsupervised adaptation of LDA for brain computer interfaces", IEEE Transactions on Biomedical Engineering, vol. 58, pp. 587-597, 2011.
59. C. Vidaurre, R. Scherer, R. Cabeza, A. Schlögl, A., and G. Pfurtscheller, "Study of discriminant analysis applied to motor imagery bipolar data", Medical & Biological Engineering & Computing, vol. 45, pp. 61-68, 2007.
60. C. Vidaurre, A. Schlögl, B. Blankertz, M. Kawanabe, and K. R. Müller, "Unsupervised adaptation of the LDA classifier for braincomputer interfaces", Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course, pp. 122-127, 2008.
61. C. Vidaurre, A. Schlögl, R. Cabeza, R. Scherer, and G. Pfurtscheller, "Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces", IEEE Transactions on Biomedical Engineering, vol. 54, pp. 550-556, 2007.
62. J. Wang, G. Xu, L. Wang, and H. Zhang, "Feature extraction of braincomputer interface based on improved multivariate adaptive autoregressive models", Proceedings of the IEEE International Conference on Biomedical Engineering and Informatics, pp. 895-898), 2010.
63. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control", Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.
64. Y. Wu, and R. Du, "Feature extraction and assessment using wavelet packets for monitoring of machining processes", Mechanical Systems and Signal Processing, vol. 10, pp. 29-53, 1996.
65. Q. Xu, H. Zhou, Y. Wang, and J. Huang, "Fuzzy support vector machine for classification of EEG signals using wavelet-based features", Medical Engineering & Physics, vol. 37, pp. 858-865, 2009.
66. B. Yang, G. Yan, R. Yan, and T. Wu, "Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition", Medical Engineering & Physics, vol. 29, pp. 48-53, 2007.
67. J. W. Yoon, S. J. Roberts, M. Dyson, and J. Q. Gan, "Adaptive classification for brain computer interface systems using sequential Monte Carlo sampling", Neural Networks, vol. 22, pp. 1286-1294, 2009.
68. Q. Zhao, L. Zhang, A. Cichocki, and J. Li, "Incremental common spatial pattern algorithm for BCI", Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 2656-2659, 2008.