Effects of Hidden Unit Sizes and Autoregressive Features in Mental Task Classification
Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
[1] Vaughan, T.M., Wolpaw, J.R., and Donchin, E., "EEG based
communications: Prospects and Problems," IEEE Transactions on
Rehabilitation Engineering, vol. 4, no. 4, pp. 425-430, December 1996.
[2] Wolpaw, J.R., Birbaumer, N., Hectderks, W.J., McFarland, D.J.,
Pecleham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson,
C.J., Vaughan, T.M. "Brain-Computer Interface Technology: A Review
of the First International Meeting," IEEE Transactions on Rehabilitation
Engineering, vol. 8 no. 2, pp. 164-173, June 2000.
[3] Anderson, C.W., Stolz, E.A., and Shamsunder, S., "Multivariate
autoregressive models for classification of spontaneous
electroencephalogram during mental tasks," IEEE Transactions on
Biomedical Engineering, vol. 45, no. 3, pp. 277-286, 1998.
[4] Currant, E., Sykacek, P., Stokes, M., Roberts, S. J., Penny, W.,
Johnsrude, I., and Owen, A.M., "Cognitive Tasks for Driving a Brain-
Computer Interfacing System: A Pilot Study," IEEE Transactions on
Neural Systems and Rehabilitation Engineering, vol. 12, no.1, pp. 48-54,
March 2003.
[5] Keirn, Z.A., and Aunon, J.I., "A new mode of communication between
man and his surroundings," IEEE Transactions on Biomedical
Engineering, vol. 37, no.12, pp. 1209-1214, December 1990.
[6] Mason, S.G., and Birch, G.E., "A General Framework for Brain-
Computer Interface Design," IEEE Transactions on Neural System and
Rehabilitation Engineering, vol. 11, no. 1, pp. 70-85, March 2003.
[7] Palaniappan, R., Paramesran, P., Nishida, S., and Saiwaki, N., "A New
Brain-Computer Interface Design Using Fuzzy ARTMAP," IEEE
Transactions on Neural System and Rehabilitation Engineering, vol. 10,
no.3, pp. 140-148, Sept. 2002.
[8] Pineda, J.A., Silverman, D.S., Vankov, A., and Hestenes, J., "Learning
to Control Brain Rhythms: Making a Brain-Computer Interface
Possible," IEEE Transactions on Neural System and Rehabilitation
Engineering, vol. 11, no.2, pp. 181-184, June 2003.
[9] Donchin, E., Spencer, K.M., and Wijesinghe, R., "The mental prosthesis:
assessing the speed of a P300-based brain-computer interface," IEEE
Transactions on Rehabilitation Engineering, vol. 8 no. 2, pp. 174-179,
June 2000.
[10] Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, "Information
Transfer Rate in a Five-Classes Brain-Computer Interface," IEEE
Transactions on Neural System and Rehabilitation Engineering, vol. 9,
No. 3, pp. 283-288, Sept. 2001.
[11] Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoses, H.,
Schlogl, A., Obermaier, B., Pregenzer, M., "Current trends in Graz
brain-computer interface (BCI) research," IEEE Transactions on
Rehabilitation Engineering, vol. 8, no. 2, pp. 216-219, June 2000.
[12] Jasper, H., "The ten twenty electrode system of the international
federation," Electroencephalographic and Clinical Neurophysiology,
vol. 10, pp. 371-375, 1958.
[13] Osaka, M., "Peak alpha frequency of EEG during a mental task: task
difficulty and hemispheric differences," Psychophysiology, vol. 21, pp.
101-105, 1984.
[14] Burg, J.P., "A new analysis technique for time series data," in Childers,
D.G. (ed), Modern Spectrum Analysis, pp.42-28, IEEE Press, 1978.
[15] Fante R.L., Signal Analysis and Estimation, John Wiley and Sons, 1988.
[16] Shiavi, R., Introduction to Applied Statistical Signal Analysis, 2nd
edition, Academic Press, 1999.
[17] Resende, F.G.V., Tokuda, K., Kaneko, M., & Nishihara, A., "RLS
Algorithm for Adaptive AR Spectrum Analysis Based on Multi-Band
Decomposition of the Linear Prediction Error," Proceedings of IEEE
Tencon-Digital Processing Application, pp. 541-546, 1996.
[18] Neumaier, A., Schneider, T., "Estimation of parameters and eigenmodes
of multivariate autoregressive models," ACM Transactions on
Mathematical Software, vol.27, no.1, pp. 27-57, March 2001.
[19] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., "Learning internal
representations by error propagation," in Rumelhart, D. E. and
McClelland, J. L. (eds), Parallel Data Processing, vol.1, The M.I.T.
Press, Cambridge, MA, pp. 318-362, 1986.
[1] Vaughan, T.M., Wolpaw, J.R., and Donchin, E., "EEG based
communications: Prospects and Problems," IEEE Transactions on
Rehabilitation Engineering, vol. 4, no. 4, pp. 425-430, December 1996.
[2] Wolpaw, J.R., Birbaumer, N., Hectderks, W.J., McFarland, D.J.,
Pecleham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson,
C.J., Vaughan, T.M. "Brain-Computer Interface Technology: A Review
of the First International Meeting," IEEE Transactions on Rehabilitation
Engineering, vol. 8 no. 2, pp. 164-173, June 2000.
[3] Anderson, C.W., Stolz, E.A., and Shamsunder, S., "Multivariate
autoregressive models for classification of spontaneous
electroencephalogram during mental tasks," IEEE Transactions on
Biomedical Engineering, vol. 45, no. 3, pp. 277-286, 1998.
[4] Currant, E., Sykacek, P., Stokes, M., Roberts, S. J., Penny, W.,
Johnsrude, I., and Owen, A.M., "Cognitive Tasks for Driving a Brain-
Computer Interfacing System: A Pilot Study," IEEE Transactions on
Neural Systems and Rehabilitation Engineering, vol. 12, no.1, pp. 48-54,
March 2003.
[5] Keirn, Z.A., and Aunon, J.I., "A new mode of communication between
man and his surroundings," IEEE Transactions on Biomedical
Engineering, vol. 37, no.12, pp. 1209-1214, December 1990.
[6] Mason, S.G., and Birch, G.E., "A General Framework for Brain-
Computer Interface Design," IEEE Transactions on Neural System and
Rehabilitation Engineering, vol. 11, no. 1, pp. 70-85, March 2003.
[7] Palaniappan, R., Paramesran, P., Nishida, S., and Saiwaki, N., "A New
Brain-Computer Interface Design Using Fuzzy ARTMAP," IEEE
Transactions on Neural System and Rehabilitation Engineering, vol. 10,
no.3, pp. 140-148, Sept. 2002.
[8] Pineda, J.A., Silverman, D.S., Vankov, A., and Hestenes, J., "Learning
to Control Brain Rhythms: Making a Brain-Computer Interface
Possible," IEEE Transactions on Neural System and Rehabilitation
Engineering, vol. 11, no.2, pp. 181-184, June 2003.
[9] Donchin, E., Spencer, K.M., and Wijesinghe, R., "The mental prosthesis:
assessing the speed of a P300-based brain-computer interface," IEEE
Transactions on Rehabilitation Engineering, vol. 8 no. 2, pp. 174-179,
June 2000.
[10] Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, "Information
Transfer Rate in a Five-Classes Brain-Computer Interface," IEEE
Transactions on Neural System and Rehabilitation Engineering, vol. 9,
No. 3, pp. 283-288, Sept. 2001.
[11] Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoses, H.,
Schlogl, A., Obermaier, B., Pregenzer, M., "Current trends in Graz
brain-computer interface (BCI) research," IEEE Transactions on
Rehabilitation Engineering, vol. 8, no. 2, pp. 216-219, June 2000.
[12] Jasper, H., "The ten twenty electrode system of the international
federation," Electroencephalographic and Clinical Neurophysiology,
vol. 10, pp. 371-375, 1958.
[13] Osaka, M., "Peak alpha frequency of EEG during a mental task: task
difficulty and hemispheric differences," Psychophysiology, vol. 21, pp.
101-105, 1984.
[14] Burg, J.P., "A new analysis technique for time series data," in Childers,
D.G. (ed), Modern Spectrum Analysis, pp.42-28, IEEE Press, 1978.
[15] Fante R.L., Signal Analysis and Estimation, John Wiley and Sons, 1988.
[16] Shiavi, R., Introduction to Applied Statistical Signal Analysis, 2nd
edition, Academic Press, 1999.
[17] Resende, F.G.V., Tokuda, K., Kaneko, M., & Nishihara, A., "RLS
Algorithm for Adaptive AR Spectrum Analysis Based on Multi-Band
Decomposition of the Linear Prediction Error," Proceedings of IEEE
Tencon-Digital Processing Application, pp. 541-546, 1996.
[18] Neumaier, A., Schneider, T., "Estimation of parameters and eigenmodes
of multivariate autoregressive models," ACM Transactions on
Mathematical Software, vol.27, no.1, pp. 27-57, March 2001.
[19] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., "Learning internal
representations by error propagation," in Rumelhart, D. E. and
McClelland, J. L. (eds), Parallel Data Processing, vol.1, The M.I.T.
Press, Cambridge, MA, pp. 318-362, 1986.
@article{"International Journal of Medical, Medicine and Health Sciences:59891", author = "Ramaswamy Palaniappan and Nai-Jen Huan", title = "Effects of Hidden Unit Sizes and Autoregressive Features in Mental Task Classification", abstract = "Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.", keywords = "Autoregressive, Brain-Computer Interface,Electroencephalogram, Neural Network.", volume = "1", number = "12", pages = "639-6", }