Classification of neural signals from sparse autoregressive features

Neurocomputing

Volumn 111, Issue , 2013, Pages 21-26

  Vidaurre, Diego ^a   Bielza, Concha ^b   Larrañaga, Pedro ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Brain computer interfaces; EEG; Group lasso; Sparse autoregressive features

Indexed keywords

AUTOREGRESSIVE COEFFICIENT; AUTOREGRESSIVE FEATURES; BINARY CLASSIFICATION; CLASSIFICATION ALGORITHM; CLASSIFICATION PERFORMANCE; ELECTROENCEPHALOGRAM SIGNALS; GROUP LASSOS; SIGNAL CLASSIFICATION;

ALGORITHMS; BRAIN COMPUTER INTERFACE; DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAPHY; LOGISTICS;

INTERFACES (COMPUTER);

ARTICLE; BRAIN COMPUTER INTERFACE; CLASSIFICATION; CLASSIFICATION ALGORITHM; DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAM; INFORMATION PROCESSING; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL VARIABLE; NEUROIMAGING; PRIORITY JOURNAL; SIGNAL PROCESSING; SPARSE AUTOREGRESSIVE FEATURE; SUPPORT VECTOR MACHINE;

EID: 84876734858     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.013     Document Type: Article

References (20)

1. Lotte F., Congedo M., Lécuyer A., Lamarche F., Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 2007, 4:1-13.
2. Wang T., Deng J., He B. Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clin. Neurophysiol. 2004, 115:2744-2753.
3. S. Chiappa, S. Bengio, HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems, in: European Symposium on Artificial Neural Networks, 2004, pp. 199-204.
4. Penny W.D., Roberts S.J., Curran E.A., Stokes M.J. EEG-based communication. a pattern recognition approach. IEEE Trans. Rehabil. Eng. 2000, 8:214-215.
5. J.P. Burg, Maximum entropy spectral analysis, in: 37th Meeting of the Society of Exploration Geophysics, 1967.
6. Huan N.J., Palaniappan R. Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design. J. Neural Eng. 2004, 1:142-150.
7. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 1996, 58:267-288.
8. Hsu N.J., Hung H.L., Chang Y.M. Subset selection for vector autoregressive processes using lasso. Comput. Stat. Data Anal. 2008, 52:3645-3657.
9. Yuan M., Lin Y. Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B 2006, 70:53-71.
10. Meier L., van de Geer S., Bühlmann P. The group lasso for logistic regression. J. R. Stat. Soc. Ser. B 2008, 70:53-71.
11. Efron B., Johnstone I., Hastie T., Tibshirani R. Least angle regression. Ann. Stat. 2004, 32(2):407-499.
12. Boyd S., Vandenberghe L. Convex Optimization 2004, Cambridge University Press.
13. T. Similä, J. Tikka, Common subset selection of inputs in multiresponse regression, in: International Joint Conference on Neural Networks, 2006, pp. 1908-1915.
14. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Data Mining, Inference, and Predictions 2008, Springer Verlag. second edition.
15. Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19(6):716-723.
16. Hastie T., Rosset S., Tibshirani R., Zhu J. The entire regularization path for the support vector machine. J. Mach. Learning Res. 2004, 5:1391-1415.
17. Lee Y., Lin Y., Wahba G. Multicategory support vector machines. Theory and application to the classification of microarray data and satellite radiance data. J. Am. Stat. Assoc. 2004, 99:67-81.
18. J. Friedman, T. Hastie, R. Tibshirani, A Note on the Group Lasso and a Sparse Group Lasso. Technical Report, Stanford University, 2010.
19. Meinshausen N. Lasso with relaxation. Comput. Stat. Data Anal. 2007, 52:374-393.
20. Keirn Z.A., Aunon J.I. A new mode of communication between man and his surroundings. IEEE Trans. Biomed. Eng. 1990, 37:1209-1214.