Classifier Combination Approach in Motion Imagery Signals Processing for Brain Computer Interface
In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.
[1] Z. A. Keirn, and J. I.Aunon, "A new mode of communication between
man and his surroundings", Biomedical Engineering, vol. 37, pp. 1209 -
1214, 1990.
[2] J. J. Vidal, "Toward Direct Brain-Computer Communication", Annual
Review of Biophysics and Bioengineering vol. 2, pp. 157-180, 1973
[3] J. J. Vidal, "Real-time detection of brain events in EEG" Proceedings of
the IEEEvol. 65, no. 5, pp. 633-641, 1977.
[4] 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, pp. 767-791, 2002.
[5] C. J. Bell, P. PradeepShenoy, R. RawichoteChalodhorn, and P. N. Rao,
"Control of a humanoid robot by a noninvasive brain-computer interface
in humans", Journal of Neural Engineeringvol. 5, no. 2, 2008.
[6] M. Zhong, F.Lotte, M. Girolami, and A. Lécuyer, "Classifying EEG for
brain computer interfaces using Gaussian processes", Pattern
Recognition Lettersvol. 29, no. 3, pp. 354-359, 2008.
[7] M. A. Lebedev, and M. A. Nicolelis, "Brain-machine interfaces: past,
present and future", TRENDS in Neurosciences, vol. 29, no. 9, pp. 536-
546, 2006.
[8] F. Mason, M. I. Norton, J. D. Van Horn, D. M. Wegner, S. T. Grafton,
and C. Macrae, "Wandering Minds: The Default Network and Stimulus-
Independent Thought", Science Magazine, vol. 315, no. 5810, pp. 393-
395, 2007.
[9] B. H. Dobkin, "Brain-computer interface technology as a tool to
augment plasticity and outcomes for neurological rehabilitation", The
Journal of Physiology vol. 579, Issue 3, pp. 637-642, 2007
[10] T. Al-ani,and D.Trad, Intelligent and Biosensors, Publisher InTech,
2010, pp. 25-66.
[11] G. Dornhege, R. Millán, T. Hinterberger, D. McFarland,and K. R.
M├╝ller, Towards Brain-Computer Interfacing, MIT Press, Cambridge
publishing, 2007, pp. 31-42.
[12] E. Pasqualotto, S. Federici,and M. O.Belardinelli, "Toward functioning
and usable brain-computer interfaces (BCIs): A literature review",
Disability and Rehabilitation: Assistive Technology, vol. 7, no. 2, pp.
89-103, 2012.
[13] N. Birbaumer, "Breaking the silence: Brain-computer interfaces (BCI)
for communication and motor control", Psychophysiology, vol. 43, pp.
517-532, 2006.
[14] M. M. Moore, "Real-World Applications for Brain Computer Interface
Technology", neural system and rehabilitation engineering, vol. 11, no.
2, pp. 162-165, 2003.
[15] D. FlotzFlotzinger, M. Pregenzer, and G. Pfurtscheller, "Feature
selection with distinction sensitive learning vector quantisation and
genetic algorithms Process", IEEE International Conference on Neural
Networks, Orlando, FL, 1994, pp. 3448-3451.
[16] B. Akinci, Realization of a cue based motor imagery brain computer
interface with its potential application to a wheelchair, Master of
Science thesis, school of natural and applied sciences of middle-east
technical university, Turkey, 2010.
[17] M. Pregenzer, Distinction sensitive learning vector quantization,
Doctoral dissertation, University of Technology, Graz, Austria, 1997,
pp. 43-60.
[18] T. Kohonen, "The Self-Orginizing Map", Proceedings of the IEEE, vol.
78, no. 9, 1990
[19] W. Tinga, Y. G.Zheng, Y. B. Hua, and S. Hong, "EEG feature extraction
based on wavelet packet decomposition for brain computer interface",
Measurement, vol. 41, pp. 618-625, 2008
[20] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition", Knowledge Discovery and Data Mining, vol. 2, pp. 121-
167, 1998.
[21] K. P. Bennett,and C. Campbell, "Support vector machines: hype or
hallelujah?", ACM SIGKDD Explorations Newsletter, vol. 2, no. 2, pp.
1-13, 2000.
[22] B. Blankertz, G. Curio,and K. R. Muller, "Classifying single trial EEG:
Towards brain computer interfacing", Advances in Neural Information
Processing Systems, vol. 14, pp.157-164, 2002
[23] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, second
edition. Wiley pree, Wiley-Interscience publisher, 2001, pp. 37-53.
[24] K. Fukunaga, introduction to Statistical Pattern Recognition, seconde
edition. Academic press, 1990, pp. 25-42.
[25] C. Anderson-s website: http:// www. cs. colostate. edu /~anderson /res
/eeg/#Contents
[26] A. Hiraiwa, K.Shimohara,and Y. Tokunaga, "EEG topography
recognition by neural networks", IEEE Engineering in Medicine and
Biology Magazine, vol. 9, no. 3, pp. 39-42, 1990.
[27] C. W. Anderson,and Z.Sijercic, "Classification of EEG signals from four
subjects during five mental tasks" Proceedings of the International
Conference on Engineering Applications of Neural Networks, Turkey,
1996, pp. 407-414.
[28] S. Tulyakov, S.Jaeger, V.Govindaraju,and D.Doermann, "Review of
Classifier Combination Methods", Machine Learning in Document
Analysis and RecognitionStudies in Computational Intelligence vol. 90,
pp. 361-386, 2008.
[29] R. R. Yager,"On ordered weighted averaging aggregation operators in
multi criteria decision making", Man and Cybernetics, vol. 18, pp. 183-
190, 1988.
[30] R. R. Yager,and D. P.Filev,"Induced ordered weighted averaging
operators. Systems, Man, and Cybernetics, Part B: Cybernetics", IEEE
Transactions on. vol. 29, no. 2, pp. 141-50. 1999.
[31] BCI Competition. http:// ida. first. fraunhofer. de/ projects/ bci/
competition ii/, 2003.
[1] Z. A. Keirn, and J. I.Aunon, "A new mode of communication between
man and his surroundings", Biomedical Engineering, vol. 37, pp. 1209 -
1214, 1990.
[2] J. J. Vidal, "Toward Direct Brain-Computer Communication", Annual
Review of Biophysics and Bioengineering vol. 2, pp. 157-180, 1973
[3] J. J. Vidal, "Real-time detection of brain events in EEG" Proceedings of
the IEEEvol. 65, no. 5, pp. 633-641, 1977.
[4] 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, pp. 767-791, 2002.
[5] C. J. Bell, P. PradeepShenoy, R. RawichoteChalodhorn, and P. N. Rao,
"Control of a humanoid robot by a noninvasive brain-computer interface
in humans", Journal of Neural Engineeringvol. 5, no. 2, 2008.
[6] M. Zhong, F.Lotte, M. Girolami, and A. Lécuyer, "Classifying EEG for
brain computer interfaces using Gaussian processes", Pattern
Recognition Lettersvol. 29, no. 3, pp. 354-359, 2008.
[7] M. A. Lebedev, and M. A. Nicolelis, "Brain-machine interfaces: past,
present and future", TRENDS in Neurosciences, vol. 29, no. 9, pp. 536-
546, 2006.
[8] F. Mason, M. I. Norton, J. D. Van Horn, D. M. Wegner, S. T. Grafton,
and C. Macrae, "Wandering Minds: The Default Network and Stimulus-
Independent Thought", Science Magazine, vol. 315, no. 5810, pp. 393-
395, 2007.
[9] B. H. Dobkin, "Brain-computer interface technology as a tool to
augment plasticity and outcomes for neurological rehabilitation", The
Journal of Physiology vol. 579, Issue 3, pp. 637-642, 2007
[10] T. Al-ani,and D.Trad, Intelligent and Biosensors, Publisher InTech,
2010, pp. 25-66.
[11] G. Dornhege, R. Millán, T. Hinterberger, D. McFarland,and K. R.
M├╝ller, Towards Brain-Computer Interfacing, MIT Press, Cambridge
publishing, 2007, pp. 31-42.
[12] E. Pasqualotto, S. Federici,and M. O.Belardinelli, "Toward functioning
and usable brain-computer interfaces (BCIs): A literature review",
Disability and Rehabilitation: Assistive Technology, vol. 7, no. 2, pp.
89-103, 2012.
[13] N. Birbaumer, "Breaking the silence: Brain-computer interfaces (BCI)
for communication and motor control", Psychophysiology, vol. 43, pp.
517-532, 2006.
[14] M. M. Moore, "Real-World Applications for Brain Computer Interface
Technology", neural system and rehabilitation engineering, vol. 11, no.
2, pp. 162-165, 2003.
[15] D. FlotzFlotzinger, M. Pregenzer, and G. Pfurtscheller, "Feature
selection with distinction sensitive learning vector quantisation and
genetic algorithms Process", IEEE International Conference on Neural
Networks, Orlando, FL, 1994, pp. 3448-3451.
[16] B. Akinci, Realization of a cue based motor imagery brain computer
interface with its potential application to a wheelchair, Master of
Science thesis, school of natural and applied sciences of middle-east
technical university, Turkey, 2010.
[17] M. Pregenzer, Distinction sensitive learning vector quantization,
Doctoral dissertation, University of Technology, Graz, Austria, 1997,
pp. 43-60.
[18] T. Kohonen, "The Self-Orginizing Map", Proceedings of the IEEE, vol.
78, no. 9, 1990
[19] W. Tinga, Y. G.Zheng, Y. B. Hua, and S. Hong, "EEG feature extraction
based on wavelet packet decomposition for brain computer interface",
Measurement, vol. 41, pp. 618-625, 2008
[20] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition", Knowledge Discovery and Data Mining, vol. 2, pp. 121-
167, 1998.
[21] K. P. Bennett,and C. Campbell, "Support vector machines: hype or
hallelujah?", ACM SIGKDD Explorations Newsletter, vol. 2, no. 2, pp.
1-13, 2000.
[22] B. Blankertz, G. Curio,and K. R. Muller, "Classifying single trial EEG:
Towards brain computer interfacing", Advances in Neural Information
Processing Systems, vol. 14, pp.157-164, 2002
[23] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, second
edition. Wiley pree, Wiley-Interscience publisher, 2001, pp. 37-53.
[24] K. Fukunaga, introduction to Statistical Pattern Recognition, seconde
edition. Academic press, 1990, pp. 25-42.
[25] C. Anderson-s website: http:// www. cs. colostate. edu /~anderson /res
/eeg/#Contents
[26] A. Hiraiwa, K.Shimohara,and Y. Tokunaga, "EEG topography
recognition by neural networks", IEEE Engineering in Medicine and
Biology Magazine, vol. 9, no. 3, pp. 39-42, 1990.
[27] C. W. Anderson,and Z.Sijercic, "Classification of EEG signals from four
subjects during five mental tasks" Proceedings of the International
Conference on Engineering Applications of Neural Networks, Turkey,
1996, pp. 407-414.
[28] S. Tulyakov, S.Jaeger, V.Govindaraju,and D.Doermann, "Review of
Classifier Combination Methods", Machine Learning in Document
Analysis and RecognitionStudies in Computational Intelligence vol. 90,
pp. 361-386, 2008.
[29] R. R. Yager,"On ordered weighted averaging aggregation operators in
multi criteria decision making", Man and Cybernetics, vol. 18, pp. 183-
190, 1988.
[30] R. R. Yager,and D. P.Filev,"Induced ordered weighted averaging
operators. Systems, Man, and Cybernetics, Part B: Cybernetics", IEEE
Transactions on. vol. 29, no. 2, pp. 141-50. 1999.
[31] BCI Competition. http:// ida. first. fraunhofer. de/ projects/ bci/
competition ii/, 2003.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:60198", author = "Homayoon Zarshenas and Mahdi Bamdad and Hadi Grailu and Akbar A. Shakoori", title = "Classifier Combination Approach in Motion Imagery Signals Processing for Brain Computer Interface", abstract = "In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.", keywords = "BCI, EEG, Classifier, Fuzzy operator, OWA.", volume = "7", number = "6", pages = "1211-6", }
