EEG signal classification: Introduction to the problem

Radioengineering

Volumn 12, Issue 3, 2003, Pages 51-55

  Šťastný, Jakub ^a   Sovka, Pavel ^a   Stančák, Andrej ^b  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b CHARLES UNIVERSITY   (Czech Republic)

Author keywords

BCI systems; EEG classification; Hidden markov models; HTK

Indexed keywords

EID: 53649096178     PISSN: 12102512     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. Ebrahini, T., Vesin, J-M., Garcia, G. Brain-computer interface. in multimedia communication. IEEE Signal Processing Magazine. 2003, no. 1, p. 14 - 24
2. Obermaier, B., Guger, C., Pfurtscheller, G. Hidden Markov models used for off line classification of EEG data. Biomedizinische Technik. 1999, vol. 44, no. 6, p. 158-162
3. Štastný, J. Analysis of states in EEG signals. M. Sc. Thesis. Prague: Czech Technical University, 2001. (in Czech)
4. Young, S. J. HTK reference manual. Cambridge University Engineering Department, 1993
5. Peltoranta, M., Pfurtscheller, G. Neural network based classification of non-averaged event-related EEG responses. Institutes for Information Processing Graz: Report Series, 1992
6. Kubat, M., Flotzinger, D., Pfurtscheller, G. Discovering patterns in EEG signals: comparative study of pattern recognition and machine learning methods. Institutes for Information Processing Graz: Report Series, 1992
7. Štastný, J., Stančák, A., Sovka, P. EEG signal classification. In Abstract Book, 23rd Annual Int. Conference of IEEE - Engineering in Medicine and Biology. Istanbul (Turkey), 2001, p. 132
8. Stančák, A., Pfurtscheller, G. The effects of handedness and type of movement on the contralateral preponderance of μ - rhythm desynchronisation. Electroencephalography and Clinical Neurophysiology, 1996, vol. 99, p. 174 - 182
9. Štastný, J., Sovka, P., Stančák, A. Extension and flexion movements: analysis and classification. Research report #Z99-2, FEL ČVUT, Department of Circuit Theory, 1999
10. Mcfarland, D. J., Mccane, L. M., David, S. V., Wolpaw, J. R. Spatial filter selection for EEG-based communication. Electroencephalography and Clinical Neurophysiology, 1997, vol. 103, p. 386 - 394
11. Penny, W. D., Roberts, S. J. Experiments with an EEG-based computer interface. Technical Report, Department of Electrical Engineering, Imperial College, London, 1999
12. Penny, W. D., Roberts, S. J., Stokes, M. J. EEG-based communication: a pattern recognition approach. Brain-Computer Interface Technology: Theory and Practice. First International Meeting, Rensselaerville, New York, 1999
13. Ramesh, S. Methods to improve the spatial resolution of EEG. Int. Journal of Bioelectromagnetism, 1999, vol. 1, p. 102 - 111
14. Wolpaw, J. R., Birbaumer, N., Mcfarland, D. J., Pfurtscheller, G., Vaughan, T. M. Brain-computer interfaces for communication and control. Clinical Neurophysiology, 2002, vol. 113, p. 767 - 791
15. Wolpaw, J. R., Mcfarland,D. J., Vaughan, T. M. Braincomputer interface research at the Wadsworth Centre. IEEE Trans. on Rehabilitation Engineering. 2000, vol. 8, no. 2, p. 222 - 226
16. Ramoser, H., Johannes Müller Gerking, J., Pfurtscheller, G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 2000, vol. 8, no. 12, p. 441 - 446
17. Schloegl, A., Lugger, K., Pfurtscheller, G. Using adaptive autoregressive parameters for a brain-computer interface equipment. Proceedings of the 19th International Conference, IEEE/ EMBS. Illinois (USA), 1997, p. 1533 - 1535
18. Štastný, J., "State of the art" of BCI systems. Research report #Z2003-3, FEL ČVUT, Department of Circuit Theory, 2003