Discrimination of Alcoholic Subjects using Second Order Autoregressive Modelling of Brain Signals Evoked during Visual Stimulus Perception
In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
[1] Jansen, B.H., Bourne, J.R., and Ward, J.W., "Autoregressive Estimation
of Short Segment Spectra for Computerized EEG Analysis," IEEE
Transactions on Biomedical Engineering, vol. 28, no. 9, pp. 630-638,
September 1981.
[2] Ning, T., and Bronzino, J.D., "Autoregressive and Bispectral Analysis
techniques: EEG Applications," IEEE Engineering in Medicine and
Biology Magazine, vol. 9 no.1, pp. 47-50, March 1990.
[3] Box, G.E.P., and Jenkins, G.M., Time Series Analysis: Forecasting and
Control Holden Day, San Francisco, 1976.
[4] Burg, J.P., "A new analysis technique for time series data," In Childers,
D.G. (ed.), Modern Spectrum Analysis, New York, IEEE Press, pp. 42-
28, 1978.
[5] 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.
[6] 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.
[7] Palaniappan, R., and Raveendran, P., "Using Genetic Algorithm to
Identify The Discriminatory Subset of Multi-channel Spectral Bands for
Visual Response", Applied Soft Computing, vol. 2, Issue 1F, pp. 48-60,
August 2002.
[8] Palaniappan, R., Raveendran, P., and Omatu, S., "VEP Optimal Channel
Selection Using Genetic Algorithm for Neural Network Classification of
Alcoholics," IEEE Transactions on Neural Network, vol. 13, issue 2,
pp.486-491, March 2002.
[9] Basar, E., Eroglu, C.B., Demiralp, T., and Schurman, M., "Time and
Frequency Analysis of the Brain-s Distributed Gamma-Band System,"
IEEE Engineering in Medicine and Biology Magazine, pp. 400-410,
July/Aug. 1995.
[10] Shiavi, R., Introduction to Applied Statistical Signal Analysis, 2nd
edition, Academic Press, 1999.
[11] Aunon, J.I., McGillem C.D., and Childers, D.G., "Signal Processing in
Event Potential Research: Averaging and Modelling," CRC Crit. Rev.
Bioeng., vol 5, pp. 323-367, 1981.
[12] Fukunaga, K., Introduction to Statistical Pattern Recognition, 2nd
edition, Academic Press, 1990.
[13] Rumelhart, D.E., and McCelland, J.L., Parallel Distributed Processing:
Exploration in the Microstructure of Cognition, MIT Press, vol. 1, 1986.
[14] Kasuba, T., "Simplified Fuzzy ARTMAP," AI Expert, vol. 8, no. 11, pp.
19-25, 1993.
[15] Jasper, H., "The ten twenty electrode system of the international
federation," Electroencephalographic and Clinical Neurophysiology,
vol. 10, pp. 371-375, 1958.
[16] Snodgrass, J.G., and Vanderwart, M., "A Standardized Set of 260
Pictures: Norms for Name Agreement, Image Agreement, Familiarity,
and Visual Complexity," Journal of Experimental Psychology: Human
Learning and Memory, vol. 6, no. 2, pp. 174-215,1980.
[17] Starr, A., and Philips, L., "Verbal and motor memory in the amnesic
syndrome," Neuropsychologia, vol. 8, pp.75-88, 1970.
[1] Jansen, B.H., Bourne, J.R., and Ward, J.W., "Autoregressive Estimation
of Short Segment Spectra for Computerized EEG Analysis," IEEE
Transactions on Biomedical Engineering, vol. 28, no. 9, pp. 630-638,
September 1981.
[2] Ning, T., and Bronzino, J.D., "Autoregressive and Bispectral Analysis
techniques: EEG Applications," IEEE Engineering in Medicine and
Biology Magazine, vol. 9 no.1, pp. 47-50, March 1990.
[3] Box, G.E.P., and Jenkins, G.M., Time Series Analysis: Forecasting and
Control Holden Day, San Francisco, 1976.
[4] Burg, J.P., "A new analysis technique for time series data," In Childers,
D.G. (ed.), Modern Spectrum Analysis, New York, IEEE Press, pp. 42-
28, 1978.
[5] 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.
[6] 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.
[7] Palaniappan, R., and Raveendran, P., "Using Genetic Algorithm to
Identify The Discriminatory Subset of Multi-channel Spectral Bands for
Visual Response", Applied Soft Computing, vol. 2, Issue 1F, pp. 48-60,
August 2002.
[8] Palaniappan, R., Raveendran, P., and Omatu, S., "VEP Optimal Channel
Selection Using Genetic Algorithm for Neural Network Classification of
Alcoholics," IEEE Transactions on Neural Network, vol. 13, issue 2,
pp.486-491, March 2002.
[9] Basar, E., Eroglu, C.B., Demiralp, T., and Schurman, M., "Time and
Frequency Analysis of the Brain-s Distributed Gamma-Band System,"
IEEE Engineering in Medicine and Biology Magazine, pp. 400-410,
July/Aug. 1995.
[10] Shiavi, R., Introduction to Applied Statistical Signal Analysis, 2nd
edition, Academic Press, 1999.
[11] Aunon, J.I., McGillem C.D., and Childers, D.G., "Signal Processing in
Event Potential Research: Averaging and Modelling," CRC Crit. Rev.
Bioeng., vol 5, pp. 323-367, 1981.
[12] Fukunaga, K., Introduction to Statistical Pattern Recognition, 2nd
edition, Academic Press, 1990.
[13] Rumelhart, D.E., and McCelland, J.L., Parallel Distributed Processing:
Exploration in the Microstructure of Cognition, MIT Press, vol. 1, 1986.
[14] Kasuba, T., "Simplified Fuzzy ARTMAP," AI Expert, vol. 8, no. 11, pp.
19-25, 1993.
[15] Jasper, H., "The ten twenty electrode system of the international
federation," Electroencephalographic and Clinical Neurophysiology,
vol. 10, pp. 371-375, 1958.
[16] Snodgrass, J.G., and Vanderwart, M., "A Standardized Set of 260
Pictures: Norms for Name Agreement, Image Agreement, Familiarity,
and Visual Complexity," Journal of Experimental Psychology: Human
Learning and Memory, vol. 6, no. 2, pp. 174-215,1980.
[17] Starr, A., and Philips, L., "Verbal and motor memory in the amnesic
syndrome," Neuropsychologia, vol. 8, pp.75-88, 1970.
@article{"International Journal of Medical, Medicine and Health Sciences:62053", author = "Ramaswamy Palaniappan", title = "Discrimination of Alcoholic Subjects using Second Order Autoregressive Modelling of Brain Signals Evoked during Visual Stimulus Perception", abstract = "In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.", keywords = "Linear Discriminant, Neural Network, VisualEvoked Potential.", volume = "1", number = "12", pages = "645-6", }