Motor Imaginary Signal Classification Using Adaptive Recursive Bandpass Filter and Adaptive Autoregressive Models for Brain Machine Interface Designs
The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
[1] L. Kauhanen, T. Nykopp, J. Lehtonen, P. Jyla¨nki, J. Heikkonen, P.
Rantanen, H. Alaranta, M. Sams, "EEG and MEG brain computer
interface for tetraplegic patients", IEEE Trans. Neural Syst. Rehabil.
Eng. 14 (2) (2006) 190-193.
[2] J. Kronegg, G. Chanel, S. Voloshynovskiy, T. Pun, "EEG-based
synchronized brain-computer interfaces: a model for optimizing the
number of mental tasks", IEEE Trans. Neural Syst. Rehabil. Eng. 15 (1)
(2007) 50-58.
[3] J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M.
Vaughan, "Brain-computer interfaces for communication and control",
Clin. Neurophysiol. 113 (2002) 767-791.
[4] G. Pfurtscheller, "EEG event-related desynchronization (ERD) and
event-related synchronization (ERS), Electroencephalogr.: Basic Prin",
Clin. Appl. (1999) 958-967.
[5] J. Vickneswaran, S. Andrew, CK. Loo, " Classification of Motor
Imaginary Tasks Using Adaptive Recursive Bandpass Filter", Proc.
International Conference on Signal Processing and Multimedia
Applications(SIGMAP2008),(2008) 113-118.
[6] SM Zhou, John Q. Gan b, F. Sepulveda, "Classifying mental tasks based
on features of higher-order statistics from EEG signals in brain computer
interface", Inform. Sci. 178 (2008) 1629-1640
[7] A. L. Blum and P. Langley, "Selection of relevant features and examples
in machine learning," Artif. Intell., vol. 97, pp. 245-271, Dec. 1997.
[8] B. H. Jansen, J. R. Bourne, J. W. Ward, "Autoregressive estimation of
short segment spectra for computerized EEG analysis," IEEE
Trans.Biomed. Eng., 28(9), 1981.
[9] G. Florian and G. Pfurtscheller, "Dynamic spectral analysis of event
related EEG data," Electroenceph. Clin. Neurophysiol. 95, (1995) 303-
309.
[10] A. Schlogl, C. Neuper, G. Pfurtscheller, "Estimating the Mutual
Information of an EEG-based Brain-Computer Interface,"
Biomedizinische Technik, 47, (2002) 3-8.
[11] M. Arnold, W. Miltner, H. Witte, R. Bauer, and C. Braun, "Adaptive AR
Modeling of Non-stationary Time Series by Means of Kalman Filtering",
IEEE Trans. Biomed. Eng., 45(5), 1998.
[12] J. Muthuswamyand, R.J. Roy, "The use of fuzzy integrals and bispectral
analysis of the electroencephalogram to predict movement under
anesthesia", IEEE Trans. Biomed. Eng. 46 (3) (1999) 291-299.
[13] R. R. Gharieb and A. Cichocki., "Segmentation and tracking of the
electro-encephalogram signal using an adaptive recursive bandpass
filter", Medical & Biological Engineering & Computing 2001,39, (2001)
237-248.
[14] R. R. Gharieb and A. Cichocki, "On-line EEG classification and sleep
spindles detection using an adaptive recursive bandpass filter", In
proceedings Acoustics, Speech, and Signal Processing (ICASSP-01)
IEEE International Conference, Vol 2, (2001).
[15] BCI Competition 2003.
<http://ida.first.fraunhofer.de/projects/bci/competition></http:>.
[16] A. Schlogl, C. Keinrath, R. Scherer, G. Pfurtscheller, "Information
transfer of an EEG-based brain-computer interface", Proc.
International IEEE EMBS Conference on Neural Engineering, (2003)
641-644.
[17] R.V. Raja Kumar and R.N. Pal, "A gradient algorithm for centerfrequency
adaptive recursive band pass filters", Proc. IEEE,73,(1985)
371-372.
[18] R.V. Raja Kumar and R.N. Pal., "Tracking of bandpass signals using
center-frequency adaptive filters", IEEE Trans. Acoust. Speech signal
processing., 38, (1990) 1710-1721.
[19] S. Haykin, Adaptive Filter Theory, 4th edition, Upper Saddle River, NJ:
Prentice Hall, 2002, Ch 10.
[20] A. Schlogl, S. Roberts, G. Pfurtscheller, "A criterion for adaptive
autoregressive models," Proceedings of the 22nd EMBS International
Conference, (2000)1581-1582.
[21] R.A. Fisher, "The use of multiple measurements in taxonomic
problems", Ann. Eugen. 7 (1936) 179-188.
[22] Burges C J C , "A tutorial on support vector machines for pattern
recognitionition", Knowl. Discov. Data Min. 2 , (1998) 121-67
[23] Bennett K P and Campbell C, " Support vector machines: hype or
hallelujah?", ACM SIGKDD Explor. Newslett. 2 ,(2000) 1-13.
[24] F Lotte, M Congedo, A L'ecuyer, F Lamarche , B Arnaldi1, "A review
of classification algorithms for EEG-based brain-computer interfaces",
J. Neural Eng. 4 (2007) R1-R13.
[25] Jain A K, Duin R P W and Mao J , " Statistical pattern recognition: a
review", IEEE Trans. Pattern Anal. Mach.Intell. 22,(2000) 4-37
[26] Lemm, S.; Schafer, C. and Curio, G., "BCI competition 2003-data set
III: probabilistic modeling of sensorimotor mu- rhythms for
classification of imaginary hand movements", Biomedical Engineering,
IEEE Transactions, 51, (2004) 1077 - 1080.
[27] P. Lingras, C. Butz, " Rough set based 1-v-1 and 1-v-r approaches to
support vector machine multi classification", Inform. Sci. 177 (2007)
3782-3798.
[28] BCI7. <www.dpmi.tu-graz.ac.at/schloegl/bci/bci7></www>
[1] L. Kauhanen, T. Nykopp, J. Lehtonen, P. Jyla¨nki, J. Heikkonen, P.
Rantanen, H. Alaranta, M. Sams, "EEG and MEG brain computer
interface for tetraplegic patients", IEEE Trans. Neural Syst. Rehabil.
Eng. 14 (2) (2006) 190-193.
[2] J. Kronegg, G. Chanel, S. Voloshynovskiy, T. Pun, "EEG-based
synchronized brain-computer interfaces: a model for optimizing the
number of mental tasks", IEEE Trans. Neural Syst. Rehabil. Eng. 15 (1)
(2007) 50-58.
[3] J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M.
Vaughan, "Brain-computer interfaces for communication and control",
Clin. Neurophysiol. 113 (2002) 767-791.
[4] G. Pfurtscheller, "EEG event-related desynchronization (ERD) and
event-related synchronization (ERS), Electroencephalogr.: Basic Prin",
Clin. Appl. (1999) 958-967.
[5] J. Vickneswaran, S. Andrew, CK. Loo, " Classification of Motor
Imaginary Tasks Using Adaptive Recursive Bandpass Filter", Proc.
International Conference on Signal Processing and Multimedia
Applications(SIGMAP2008),(2008) 113-118.
[6] SM Zhou, John Q. Gan b, F. Sepulveda, "Classifying mental tasks based
on features of higher-order statistics from EEG signals in brain computer
interface", Inform. Sci. 178 (2008) 1629-1640
[7] A. L. Blum and P. Langley, "Selection of relevant features and examples
in machine learning," Artif. Intell., vol. 97, pp. 245-271, Dec. 1997.
[8] B. H. Jansen, J. R. Bourne, J. W. Ward, "Autoregressive estimation of
short segment spectra for computerized EEG analysis," IEEE
Trans.Biomed. Eng., 28(9), 1981.
[9] G. Florian and G. Pfurtscheller, "Dynamic spectral analysis of event
related EEG data," Electroenceph. Clin. Neurophysiol. 95, (1995) 303-
309.
[10] A. Schlogl, C. Neuper, G. Pfurtscheller, "Estimating the Mutual
Information of an EEG-based Brain-Computer Interface,"
Biomedizinische Technik, 47, (2002) 3-8.
[11] M. Arnold, W. Miltner, H. Witte, R. Bauer, and C. Braun, "Adaptive AR
Modeling of Non-stationary Time Series by Means of Kalman Filtering",
IEEE Trans. Biomed. Eng., 45(5), 1998.
[12] J. Muthuswamyand, R.J. Roy, "The use of fuzzy integrals and bispectral
analysis of the electroencephalogram to predict movement under
anesthesia", IEEE Trans. Biomed. Eng. 46 (3) (1999) 291-299.
[13] R. R. Gharieb and A. Cichocki., "Segmentation and tracking of the
electro-encephalogram signal using an adaptive recursive bandpass
filter", Medical & Biological Engineering & Computing 2001,39, (2001)
237-248.
[14] R. R. Gharieb and A. Cichocki, "On-line EEG classification and sleep
spindles detection using an adaptive recursive bandpass filter", In
proceedings Acoustics, Speech, and Signal Processing (ICASSP-01)
IEEE International Conference, Vol 2, (2001).
[15] BCI Competition 2003.
<http://ida.first.fraunhofer.de/projects/bci/competition></http:>.
[16] A. Schlogl, C. Keinrath, R. Scherer, G. Pfurtscheller, "Information
transfer of an EEG-based brain-computer interface", Proc.
International IEEE EMBS Conference on Neural Engineering, (2003)
641-644.
[17] R.V. Raja Kumar and R.N. Pal, "A gradient algorithm for centerfrequency
adaptive recursive band pass filters", Proc. IEEE,73,(1985)
371-372.
[18] R.V. Raja Kumar and R.N. Pal., "Tracking of bandpass signals using
center-frequency adaptive filters", IEEE Trans. Acoust. Speech signal
processing., 38, (1990) 1710-1721.
[19] S. Haykin, Adaptive Filter Theory, 4th edition, Upper Saddle River, NJ:
Prentice Hall, 2002, Ch 10.
[20] A. Schlogl, S. Roberts, G. Pfurtscheller, "A criterion for adaptive
autoregressive models," Proceedings of the 22nd EMBS International
Conference, (2000)1581-1582.
[21] R.A. Fisher, "The use of multiple measurements in taxonomic
problems", Ann. Eugen. 7 (1936) 179-188.
[22] Burges C J C , "A tutorial on support vector machines for pattern
recognitionition", Knowl. Discov. Data Min. 2 , (1998) 121-67
[23] Bennett K P and Campbell C, " Support vector machines: hype or
hallelujah?", ACM SIGKDD Explor. Newslett. 2 ,(2000) 1-13.
[24] F Lotte, M Congedo, A L'ecuyer, F Lamarche , B Arnaldi1, "A review
of classification algorithms for EEG-based brain-computer interfaces",
J. Neural Eng. 4 (2007) R1-R13.
[25] Jain A K, Duin R P W and Mao J , " Statistical pattern recognition: a
review", IEEE Trans. Pattern Anal. Mach.Intell. 22,(2000) 4-37
[26] Lemm, S.; Schafer, C. and Curio, G., "BCI competition 2003-data set
III: probabilistic modeling of sensorimotor mu- rhythms for
classification of imaginary hand movements", Biomedical Engineering,
IEEE Transactions, 51, (2004) 1077 - 1080.
[27] P. Lingras, C. Butz, " Rough set based 1-v-1 and 1-v-r approaches to
support vector machine multi classification", Inform. Sci. 177 (2007)
3782-3798.
[28] BCI7. <www.dpmi.tu-graz.ac.at/schloegl/bci/bci7></www>
@article{"International Journal of Medical, Medicine and Health Sciences:62114", author = "Vickneswaran Jeyabalan and Andrews Samraj and Loo Chu Kiong", title = "Motor Imaginary Signal Classification Using Adaptive Recursive Bandpass Filter and Adaptive Autoregressive Models for Brain Machine Interface Designs", abstract = "The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
", keywords = "Adaptive autoregressive, adaptive bandpass filter, brain machine Interface, EEG, motor imaginary.", volume = "1", number = "5", pages = "311-8", }
