Discriminative learning of propagation and spatial pattern formotor imagery EEG analysis

Neural Computation

Volumn 25, Issue 10, 2013, Pages 2709-2733

  Li, Xinyang ^a   Zhang, Haihong ^b   Guan, Cuntai ^b   Ong, Sim Heng ^c   Ang, Kai Keng ^b   Pan, Yaozhang ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BRAIN COMPUTER INTERFACE; DISCRIMINATION LEARNING; ELECTROENCEPHALOGRAPHY; HUMAN; IMAGINATION; LEARNING; MOVEMENT (PHYSIOLOGY); NEUROSCIENCE; PHYSIOLOGY; SIGNAL PROCESSING; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN-COMPUTER INTERFACES; DATA INTERPRETATION, STATISTICAL; DISCRIMINATION LEARNING; ELECTROENCEPHALOGRAPHY; HUMANS; IMAGINATION; LEARNING; MODELS, STATISTICAL; MOVEMENT; NEUROSCIENCES; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84887363438     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00500     Document Type: Article

References (42)

1. Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012). Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Frontiers in Neuroscience, 6(39).
2. Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008). Filter bank common spatial pattern (FBCSP) in brain-computer interface. In Proceedings of the IEEE International Joint Conference on Neural Networks and Computational Intelligence (pp. 2390-2397). Piscataway, NJ: IEEE.
3. Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2012). Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs. Pattern Recognition, 45(6), 2137-2144.
4. Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. (2011). Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Transactions on Biomedical Engineering, 58(6), 1865-1873.
5. Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. (2013a). EEG data space adaptation to reduce inter-session non-stationarity in brain-computer interface. Neural Computation, 25, 2146-2171.
6. Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. (2013b). Optimizing spatial filters by minimizing within-class dissimilarities in EEG-based BCI. IEEE Transactions on Neural Networks and Learning Systems, 24(4), 610-619.
7. Astolfi, L., Cincotti, F., Mattia, D., de Vico Fallani, F., Salinari, S., Ursino, M., & Babiloni, F. (2006). Estimation of the cortical connectivity patterns during the intention of limb movements. IEEE Engineering in Medicine and Biology Magazine, 25(4), 32-38.
8. Baccala, L. A., & Sameshima, K. (2001). Partial directed coherence: A new concept in neural structure determination. Biological Cybernetics, 84, 463-474.
9. Bahramisharif, A., van Gerven, M. A. J., Schoffelen, J. M., Ghahramani, Z., & Heskes, T. (2012). The dynamic beamformer.Machine learning and interpretation in neuroimaging. In G. Langs, I. Rish, M. Grosse-Wentrup, & B. Murphy (Eds.), Lecture notes in computer science (vol. 7263, pp. 148-155). Berlin: Springer.
10. Bamdadian, A., Guan, C., Ang, K.K., & Xu, J. (2012). Online semi-supervised learning with kl distance weighting formotor imagery-based BCI. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2732-2735). Piscataway, NJ: IEEE.
11. Blankertz, B., Tomika, R., Lemm, S., Kawanabe, M., & Muller, K. R. (2008). Optimizing spatial filters for robust EEG single trial-trial analysis. IEEE Signal Processing Magazine, 25(1), 41-56.
12. Chen, H., Yang, Q., Liao, W., Gong, Q., & Shen, S. (2009). Evaluation of the effective connectivity of supplementary motor areas duringmotor imagery using Granger causality mapping. NeuroImage, 47(4), 1844-1853.
13. Dornhege, G., Blankertz, B., Krauledat, M., Losch, F., Curio, G., & Muller, K.-R. (2006). Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Transactions on Biomedical Engineering, 53(11), 2274-2281.
14. Dyrholm, M., Makeig, S., & Hansen, L.K. (2007). Convolutive ICA for spatiotemporal analysis of EEG. Neural Computation, 19, 934-955.
15. Ewald, A., Marzetti, L., Zappasodi, F., Meinecke, F. C., & Nolte, G. (2012). Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space. NeuroImage, 60(1), 476-488.
16. Formaggio, E., Storti, S. F., Cerini, R., Fiaschi, A., & Manganotti, P. (2010). Brain oscillatory activity during motor imagery in EEG-fMRI coregistration. Magnetic Resonance Imaging, 28(10), 1403-1412.
17. Gerkinga, J. M., Pfurtscheller, G., & Flyvbjergc, H. (1999). Designing optimal spatial filters for single-trial EEG classiffication in a movement task. Clinical Neurophysiology, 110, 787-798.
18. Ginter, J. Jr., Blinowska, K. J., Kaminski, M., & Durka, P. J. (2001). Phase and amplitude analysis in time-requency space-pplication to voluntary fingermovement. Journal of Neuroscience Methods, 110(1-2), 113-124.
19. Gomez-Herrero, G., Atienza, M., Egiazarian, K., & Cantero, J. L. (2008). Measuring directional coupling between EEG sources. NeuroImage, 43(3), 497-508.
20. Grosse-Wentrup, M. (2009). Understanding brain connectivity patterns during motor imagery for brain-computer interfacing. InD.Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems, 21 (pp. 561-568). Cambridge, MA: MIT Press.
21. Guler, I., Kiymik, M. K., Akin, M., & Alkan, A. (2001). AR spectral analysis of EEG signals by using maximum likelihood estimation. Computers in Biology and Medicine, 31(6), 441-450.
22. Gysels, E., & Celka, P. (2007). Phase synchronization for the recognition of mental tasks in a braincomputer interface. IEEE Transactions on Rehablitation Engineering, 12(4), 406-415.
23. Haufe, S., Tomioka, R., Nolte, G., Muller, K.-R., & Kawanabe, M. (2010). Modeling sparse connectivity between underlying brain sources for EEG/MEG. IEEE Transactions on Biomedical Engineering, 57(8), 1954-1963.
24. Jeannerod, M. (1995). Mental imagery in the motor context. Neuropsychologia, 33, 1419-1432.
25. Kaminski, M., & Blinowska, K. (1991). A new method of the description of the information flow in the brain structures. Biological Cybernetics, 65, 203-210.
26. Kaminski, M., Ding, M., Truccolo, W. A., & Bressle, S. (2001). Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological Cybernetics, 85, 145-157.
27. Koles, Z. J. (1991). The quantitative extraction and toporraphic mapping of the abnormal components in the clinical EEG. Electroencephalography and Clinical Neurophysiology, 79, 440-447.
28. Kus, R., Kaminski, M., & Blinowska, K. J. (2004). Determination of EEG activity propagation: Pair-wise versus multichannel estimate. IEEE Transactions on Biomedical Engineering, 51(9), 1501-1510.
29. Lemm, S., Blankertz, B., Curio, G., & Muller, K.-R. (2005). Spatio-spectral filters for improving the classification of single trial EEG. IEEE Transactions on Biomedical Engineering, 52(9), 1541-1548.
30. Li, Y., & Guan, C. (2006). An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces. Neural Computation, 18, 2730-2761.
31. Llera, A., Gomez, V., & Kappen, H. J. (2012). Adaptive classification on braincomputer interfaces using reinforcement signals. Neural Computation, 24, 2900-2923.
32. Lo, A.C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., & Peduzzi, P. (2010). Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 362, 1772-1783.
33. Mørup, M., Madsen, K. H., & Hansen, L. K. (2009). Latent causal modelling of neuroimaging data. In NIPS Workshop on Connectivity Inference in Neuroimaging.
34. Pfurtscheller, G., Brunner, C., Schlogl, A., & da Silva, F. H. L. (2006). Mu rhythm (de)synchronization andEEGsingle-trial classification of different motor imagery tasks. NeuroImage, 31(1), 153-159.
35. Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.
36. Schlogl, A., & Supp, G. (2006). Analyzing event-related EEG data with multivariate autoregressive parameters. Progress in Brain Research, 159, 135-147.
37. Schneider, T., & Neumaier, A. (2001). Algorithm 808: Arfit-a Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software (TOMS), 6, 58-65.
38. Stavrinou, M., Moraru, L., Cimponeriu, L., Stefania, P. D., & Bezerianos, A. (2007). Evaluation of cortical connectivity during real and imagined rhythmic finger tapping. Brain Topography, 19(3), 137-145.
39. Thomas, K. P., Guan, C., Lau, C. T., Vinod, A. P., & Ang, K. K. (2009). A new discriminative common spatial pattern method for motor imagery brain-computer interfaces. IEEE Transactions on Biomedical Engineering, 56(11), 2730-2733.
40. Vidaurre, C., Sannelli, C., Muller, K.-R., & Blankertz, B. (2011). Machine-learningbased coadaptive calibration for brain-computer interfaces. Neural Computation, 23, 791-816.
41. Wei, Q., Wang, Y., Gao, X., & Gao, S. (2007). Amplitude and phase couplingmeasures for feature extraction in an EEG-based brainc̈computer interface. Journal of Neural Engineering, 4, 120-129.
42. Xu, L., Stoica, P., Li, J., Bressler, S. L., Shao, X., & Ding, M. (2009). Aseo: Amethod for the simultaneous estimation of single-trial event-related potentials and ongoing brain activities. IEEE Transactions on Biomedical Engineering, 56(1), 111-121.