Nonlinear dimensionality reduction of electroencephalogram (EEG) for brain computer interfaces

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Volumn , Issue , 2009, Pages 2486-2489

  Teli, Mohammad Nayeem ^a   Anderson, Charles ^a  

^a University Services Building   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN COMPUTER INTERFACE; CLASSIFICATION (OF INFORMATION); DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAPHY; INTERFACES (COMPUTER); MATHEMATICAL TRANSFORMATIONS; NUMERICAL ANALYSIS;

ELECTRO-ENCEPHALOGRAM (EEG); ELECTROENCEPHALOGRAM SIGNALS; LOW-DIMENSIONAL REPRESENTATION; LOW-DIMENSIONAL SPACES; NON-LINEAR TRANSFORMATIONS; NONLINEAR DIMENSIONALITY REDUCTION; NONLINEAR PROJECTIONS; QUADRATIC DISCRIMINANT ANALYSIS;

BIOMEDICAL SIGNAL PROCESSING;

EID: 77950968670     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2009.5334802     Document Type: Conference Paper

References (13)

1. T. Vaughan and J. Wolpaw, "Guest Editorial, The Third International Meeting on Brain-Computer Interface Technology: Making a Difference," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, no.2, 2006.
2. L. Parra, C. Christoforou, A. Gerson, M. Dyrholm, L. An, M. Wagner, M. Philiastides, and P. Sajda, "Spatiotemporal Linear Decoding of Brain State," IEEE Signal Processing Magazine, vol.25, no.1, pp. 107-115, 2008.
3. A. Gramfort and M. Clerc, "Low Dimensional Representations of MEG/EEG Data Using Laplacian Eigenmaps," Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, NFSI-ICFBI., pp. 169-172, Oct. 2007.
4. M. A. Kramer, "Autoassociative Neural Networks," Computers and Chemical Engineering, vol.16, pp. 313-328, 1992.
5. S. Devulapalli, "Non-Linear Principal Component Analysis and Classification of EEG During Mental Tasks," Master's thesis, Colorado State University, 1996.
6. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009.
7. V. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag New York Inc., 1995.
8. F. Lotte, H. Mouchere, and A. Lecuyer, "Pattern Rejection Strategies for the Design of Self-Paced EEG-based Brain-Computer Interfaces," in International Conference on Pattern Recognition, ICPR08, Tampa, Florida, USA, 2008, pp. 1-5.
9. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, "A Review of Classification Algorithms for EEG-Based BrainComputer Interfaces," J. Neural Eng., vol.4, no.2, pp. R1-R13, June 2007. [Online]. Available: http://dx.doi.org/10.1088/1741-2560/4/2/R01
10. M. F. Møller, "A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning," Neural Networks, vol.6, pp. 525-533, 1993.
11. B. M. Wilamowski, S. Iplikci, O. Kaynak, and M. O. Efe, "An Algorithm for Fast Convergence in Training Neural Networks," in International Joint Conference on Neural Networks, vol.3, 2001, pp. 1778-1782.
12. Z. Keirn and J. Aunon, "A New Mode of Communication Between Man and his Surroundings," IEEE Transactions on Biomedical Engineering, vol.37, no.12, pp. 1209-1214, 1990.
13. M. N. Teli, "Dimensionality Reduction and Classification of Time EmbeddedEEG Signals," Master's thesis, Colorado State University, 2007.