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Jakub Štastný, Pavel Sovka, "High-Resolution Movement EEG Classification", Computational Intelligence and Neuroscience, vol. 2007, Article ID 054925, 12 pages, 2007. https://doi.org/10.1155/2007/54925
High-Resolution Movement EEG Classification
The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.
- L. Pickup, “Machine learning approaches for brain-computer interfacing,” Tech. Rep. PARG-02-01, Pattern Analysis and Machine Leraning Group, Robotics Research Group, Department of engineering science, University of Oxford, Oxford, UK, May 2002.
- B. Blankertz, G. Dornhege, M. Krauledat et al., “The Berlin brain-computer interface: EEG-based communication without subject training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 147–152, 2006.
- W. D. Penny and S. J. Roberts, “Experiments with an EEG-based computer interface,” Tech. Rep., Department of Electrical Engineering, Imperial College, London, UK, July 1999.
- W. D. Penny, S. J. Roberts, and M. J. Stokes, “EEG-based communication: a pattern recognition approach,” in Brain-Computer Interface Technology: Theory and Practice. First International Meeting, Rensselaerville, NY, USA, May 1999.
- A. Schlögl, K. Lugger, and G. Pfurtscheller, “Using adaptive autoregressive parameters for a brain-computer interface equipment,” in Proceedings of the 19th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '97), pp. 1533–1535, Chicago, Ill, USA, October-Novermber 1997.
- C. W. Anderson, E. A. Stolz, and S. Shasunder, “Discriminating mental tasks using EEG represented by AR model,” in Proceedings of IEEE 17th Annual Conference on Engineering in Medicine and Biology Society (IEMBS '95), vol. 2, pp. 875–876, Montreal, Quebec, Canada, September 1995.
- C. W. Anderson and Z. Sijerčíc, “Classification of EEG signals from four subjects during five mental tasks,” in Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN '96), pp. 407–414, Turku, Finland, June 1996.
- 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, no. 6, pp. 767–791, 2002.
- J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, “Brain-computer interface research at the Wadsworth Center,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 222–226, 2000.
- J. D. Bayllis and D. H. Ballard, “Recognizing evoked potentials in a virtual environment,” in Advances in Neural Information Processing Systems 12, vol. 12, pp. 3–9, Denver, Colo, USA, November-December 2000.
- G. Schalk, J. R. Wolpaw, D. J. McFarland, and G. Pfurtscheller, “EEG-based communication:presence of an error potential,” Clinical Neurophysiology, vol. 111, no. 12, pp. 2138–2144, 2000.
- U. Hoffman, J.-M Versin, K. Deserens, and T. Ebrahimi, “An efficient P300-based brain computer interface for disabled subjects,” preprint, 2007, http://bci.epfl.ch/efficientp300bci.html.
- W. D. Penny, S. J. Roberts, and M. J. Stokes, “Imagined hand movements identified from the EEG -rhythm,” Tech. Rep., Department of Electrical Engineering, Imperial College, London, UK, August 1998.
- D. J. McFarland, L. A. Miner, T. M. Vaughan, and J. R. Wolpaw, “ and rhythm topographies during motor imagery and actual movements,” Brain Topography, vol. 12, no. 3, pp. 177–186, 2000.
- J. Šťastný, J. Zejbrdlich, and P. Sovka, “Optimal parameterization selection for the brain-computer interface,” in Proceedings of the 4th WSEAS International Conference of Applications of Electrical Engineering , pp. 300–304, Prague, Czech Republic, March 2005.
- J. Šťastný, P. Sovka, and A. Stančák, “EEG signal classification,” in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 2020–2023, Istanbul, Turkey, October 2001.
- J. Doležal, J. Šťastný, and P. Sovka, “Recognition of direction of finger movement from EEG signal using markov models,” in Proceedings of the 3rd European Medical&Biological Engineering Conference (EMBEC '05), vol. 11, pp. 1492-1–1492-6, Prague, Czech Republic, November 2005.
- H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441–446, 2000.
- P. Hluštík, A. Solodkin, R. P. Gullapalli, D. C. Noll, and S. L. Small, “Somatotopy in human primary motor and somatosensory hand representations revisited,” Cerebral Cortex, vol. 11, no. 4, pp. 312–321, 2001.
- G. Pfurtscheller, K. Zalaudek, and C. Neuper, “Event-related beta synchronization after wrist, finger and thumb movement,” Electroencephalography and Clinical Neurophysiology - Electromyography and Motor Control, vol. 109, no. 2, pp. 154–160, 1998.
- A. Stančák, B. Feige, C. H. Lücking, and R. Kristeva-Feige, “Oscillatory cortical activity and movement-related potentials in proximal and distal movements,” Clinical Neurophysiology, vol. 111, no. 4, pp. 636–650, 2000.
- A. Stančák and G. Pfurtscheller, “The effects of handedness and type of movement on the contralateral preponderance of -rhythm desynchronisation,” Electroencephalography and Clinical Neurophysiology, vol. 99, no. 2, pp. 174–182, 1996.
- A. Stančák, A. Riml, and G. Pfurtscheller, “The effects of external load on movement-related changes of the senorimotor EEG rhythms,” Electroencephalography and Clinical Neurophysiology, vol. 102, no. 6, pp. 495–504, 1997.
- G. Pfurtscheller, A. Stančák, and C. Neuper, “Post-movement beta synchronization: a correlate of an idling motor area?” Electroencephalography and Clinical Neurophysiology, vol. 98, no. 4, pp. 281–293, 1996.
- G. Pfurtscheller, A. Stančák, and G. Edlinger, “On the existence of different types of central rhythms below 30 Hz,” Electroencephalography and Clinical Neurophysiology, vol. 102, no. 4, pp. 316–325, 1997.
- A. Stančák, “Event-related desynchronization of the -rhythm in extension and flexion finger movements,” in 11th International Congress of Electromyography and Clinical Neurophysiology, Supplements to Clinical Neurophysiology, 53 , pp. 636–650, Prague, Czech Republic, September 2000.
- H. J. Steingrüber and G. A. Lieuert, Hand-Dominanztest, Hogrefe, 2nd edition, 1976.
- R. Srinivasan, “Methods to improve the spatial resolution of EEG,” International Journal of Bioelectromagnetism, vol. 1, no. 1, pp. 102–111, 1999.
- G. Pfurtscheller, Digital Biosignal Processing, Elsevier Science, Amsterdam, The Netherlands, 2nd edition, 1991, chapter 17.
- B. Hjorth, “An on-line transformation of EEG scalp potentials into orhtogonal source derivations,” Electroencephalography and Clinical Neurophysiology, vol. 39, no. 5, pp. 526–530, 1975.
- J. Šťastný and P. Sovka, “The 3D surface Laplacian filtration with integrated sampling error compensation,” Signal Processing, vol. 87, no. 1, pp. 51–60, 2007.
- J. Uhlíř and P. Sovka, Digital Signal Processing, Czech Technical University, Prague, Czech Republic, 2nd edition, 2002.
- L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989.
- J. Šťastný, P. Sovka, and A. Stančák, “EEG signal classification: introduction to the problem,” Radioengineering, vol. 12, no. 3, pp. 51–55, 2003.
- J. Šťastný, P. Sovka, and A. Stančák, “EEG signal classification and segmentation by means of hidden markov models,” in Proceedings of 16th Biennial International EURASIP Conference on Analysis of Biomedical Signals and Images (BIOSIGNAL '02), pp. 415–417, Brno, Czech Republic, June 2002.
- S. J. Young, HTK Reference Manual, Cambridge University Engineering Department, Cambridge, UK, 1993.
- O. Konopka, J. Šťastný, and P. Sovka, “Movement-related EEG separation using independent component analysis,” in Proceedings of the 3rd European Medical & Biological Engineering Conference (EMBEC '05) , vol. 11, pp. 1471-1–1471-6, Prague, Czech Republic, Novermber 2006.
Copyright © 2007 Jakub Štastný and Pavel Sovka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.