Electrophysiological impairments of alcoholism have been researched extensively. However, there is none or few reported research on screening methods for chronic alcoholic subjects. Since chronic alcoholics have serious brain dysfunction, a method to screen for them during specific job applications that require good memory, concentration and/or decision making would be useful. In this paper, a method is proposed to discriminate chronic alcoholic from non-alcoholic subjects while they are sober. Energies of electroencephalogram signals in multiple gamma bands recorded while the subjects performed a picture recognition task are used as features by a neural network to detect the chronic alcoholic subjects. Leave one out cross validation strategy reveals that alcoholics could be discriminated from non-alcoholics with accuracy of 94.55%. This pilot study has shown the potential of the method which could be further developed for use in automatic alcoholic screening procedures.Facultad de Informátic

Palaniappan, Ramaswamy

Journal of Computer Science and Technology

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

Screening for Chronic Alcoholic Subjects Using Multiple Gamma Band EEG: A Pilot Study  Ramaswamy Palaniappan  Department of Computer Science, University of Essex Wivenhoe Park, CO4 3SQ, Colchester, United Kingdom Email: rpalan@essex.ac.uk  ABSTRACT Electrophysiological impairments of alcoholism have been researched extensively. However, there is none or few reported research on screening methods for chronic alcoholic subjects. Since chronic alcoholics have serious brain dysfunction, a method to screen for them during specific job applications that require good memory, concentration and/or decision making would be useful. In this paper, a method is proposed to discriminate chronic alcoholic from non-alcoholic subjects while they are sober. Energies of electroencephalogram signals in multiple gamma bands recorded while the subjects performed a picture recognition task are used as features by a neural network to detect the chronic alcoholic subjects. Leave one out cross validation strategy reveals that alcoholics could be discriminated from non-alcoholics with accuracy of 94.55%. This pilot study has shown the potential of the method which could be further developed for use in automatic alcoholic screening procedures.  Keywords: Alcoholic subject discrimination, Electroencephalogram, Gamma band energy, Neural network  1. INTRODUCTION Alcohol abuse is associated with many health and social problems and brings high economic costs. It is estimated that 9–20% of patients admitted to a primary healthcare service are screened positive for alcohol abuse [1]. Analyses on the brain dysfunction caused by alcoholism have been researched extensively. Chronic alcoholics, who have been drinking heavily for many years, have been shown to have problems with memory, attention and decision making even after a period of abstinence [2], [4], [10]. Therefore, screening for alcoholics could be of importance during certain job recruitments that need some of these skills (like machine operators requiring concentration).  However, there are not many reported studies on methods for detecting chronic alcoholics from the rest of the population when they are sober. This must not be confused with methods for detecting alcoholics under the influence of alcohol (using alcohol levels in blood, for example like the breath analyser test). The only related study to the one here was that by [8] who investigated a method to select optimal channels using genetic algorithm, where they particularly studied a two class problem using groups of alcoholics and non-alcoholics. Both the alcoholics and non-alcoholics were included in the training and testing data sets in this study and their objective was not on the individual discrimination of alcoholic subjects.  In this paper, an attempt is made to discriminate chronic alcoholic subjects from non-alcoholic subjects using features from brain signals. The validity of the method is shown through an analysis involving several stages of pre-processing, feature extraction and classification methodologies.   2. METHODOLOGY In this study, electroencephalogram (EEG) data from ten alcoholics and ten non-alcoholics were used. This data set were recorded by the authors in [10], for their studies on establishing a marker to analyse the short-term visual memory deficits in alcoholics.     The subjects The alcoholics tested had been abstinent for a minimum period of one month (through closed ward detention). Therefore, all alcoholics were fully detoxified and had no alcohol available for that period of hospitalisation. Alcoholic individuals were excluded from the study if they had history of drug dependence, major psychiatric illness, or other diseases related to overt liver, metabolic, vascular and neurological. Most of the alcoholics had been drinking heavily for a minimum of 15 years. The diagnosis of alcohol abuse was made by the intake psychiatrist of the Addictive Disease Hospital in Brooklyn, USA according to DSM-III criteria. The alcoholics were non-amnesics and also not substance abusers. They were also matched for socioeconomic status.   EEG data The EEG signals were recorded non-invasively from 61 active plus 3 reference channels from the scalp. The positions of 19 electrodes were in conjunction with the 10-20 international system and additional electrodes were placed in between these to obtain the total of 61 electrodes, covering most of the recordable activity of the brain. Figure 1 shows the location of these electrodes.   JCS&T Vol. 7 No. 2                                                                                                                                 April 2007182FP1T8AF7F7FC7OZO1TP7T5P07CZCPZPZPOZAFZFZFCZFP2T10AF8F8FC8O2TP8T6PO8C5 C3 C1 C2 C4 C6FC5 FC3 FC1F5 F3 F1AF1FC2 FC4 FC6F2AF2F6F4CP2 CP4 CP6P2 P4 P6PO4PO3CP1CP3CP5P1P3P5FPZ  Figure 1. 61 active electrode system.  EEG signals are actually potentials exhibited by neuronal excitations in the cortex and these were recorded with the subjects observing drawings of common black and white objects from the Snodgrass and Vanderwart (SV) picture set [9]. The SV picture set represent common black and white objects, such as, for instance, airplane, banana, and ball. These were chosen according to a set of rules that provides consistency of pictorial contents. They have been standardised based on the variables of central relevance to memory and cognitive processing. These objects can be named i.e. they have definite verbal labels.  All the subjects were asked to remember or recognise the stimulus. Stimulus duration of every picture was 300 ms. All the stimuli were shown using a display located 1 meter away from the subjects. One-second EEG measurements (sampled at 256 Hz) after each stimulus onset were stored. Figure 2 shows some examples of the picture stimuli.       Figure 2. Examples of stimuli from SV set [9].  Pre-processing EEG signals contaminated with eye blink artifacts were not considered in the approach, and were detected using a 100 PV threshold. This is a common threshold value in EEG studies, and is used since blinking produces 100-200 PV potential lasting 250 milliseconds [7]. A total of 40 artifact free trials were considered for every subject, to make a total 800 EEG signals.    Feature extraction EEG signals were filtered using Elliptic filter. Filtering was performed twice; once forward and once reverse to remove the non-linear phase effects. Frequency range was from 20-70 Hz because of the reported existence of gamma band oscillations in this range [3].  This frequency range was split into 3 gamma bands: low-gamma (20-30 Hz), mid-gamma (30-50 Hz) and high-gamma (50-70 Hz). Order 5 was sufficient to obtain pass-bands of 20-30 Hz, 30-50 Hz and 50-70 Hz with minimum stop-band attenuation of 30 dB at 5 Hz below and above the pass-band ranges. The ripple in the pass-bands was kept below 0.1 dB. Energy of the filtered EEG signal in each low-gamma, mid-gamma and high-gamma bands from each channel were computed and normalised with the total energy in each band from 61 channels to form a total of 183 EEG features. To reduce the number of features, a simple technique using t-test1 analyses (with significance of 0.01) were used to obtain the most discriminant channels for each band. For each gamma band, the channels that gave significant difference between alcoholic features and non-alcoholic features were noted and only features from these channels for the particular gamma band were used in classification. There were 33 significant channels for low-gamma, 36 significant channels for mid-gamma and 54 significant channels for high-gamma, giving a total of 123 features for each EEG signal. There were only 21 common significant channels for the 3 gamma bands, the rest of the significant channels differed.  Neural network classification  Features from nine alcoholics and nine non-alcoholics (totalling 720 feature set, each with 123 features) were used in training the multilayer-perceptron (MLP) neural network (NN), while the rest of the features from the other alcoholic and other non-alcoholic (totalling 80 feature set) were used in the testing. The feature sets used in training and testing were whitened to remove correlation.  Signal whitening seeks to obtain the EEG matrix x~ , such that the covariance of matrix x~  becomes the identity matrix  I)xxE( T  ~~ . (1)  A common whitening method is via the eigenvalue decomposition of the covariance matrix TT FDF)E(xx  , where F  is the orthogonal matrix of eigenvectors of )E(xxT  and D  is the diagonal matrix of its eigenvalues, ).,....,( 1 ndddiag D Whitening can now be performed using                                                   1 The features were assumed to be of normal distribution. JCS&T Vol. 7 No. 2                                                                                                                                 April 2007183 xFFDx T1/2  ~ . (2)  These whitened features were then normalised to the range [-1,1], using maximum and minimum values of each feature, with the idea to improve the NN training; MLP NN was trained by the backpropagation algorithm (BP) with adaptive learning rate to classify the features. The hidden units (HU) were varied from 20 to 100 in steps of 20. The number of input units was set at 123. The output units were set at two: one for each alcoholic or non-alcoholic category. One-hot encoding was used for the target values (either 0 or 1). The training was conducted until the mean-square error fell below a threshold of 0.01. The MLP-BP training and testing procedures were repeated for ten times but with training and testing feature sets from different alcoholics and different non-alcoholics for each time. In other words, feature sets from different nine alcoholics and nine different non-alcoholics were used for training, while feature sets from the other alcoholic and other non-alcoholic were used for testing each time. This procedure of repeating the training and testing is commonly referred as the leave one out cross validation, which is useful in increasing the reliability of the classification results.  3. RESULTS Table 1 shows the MLP-BP NN training, testing times and the classification accuracies for varying HU sizes. Only the averaged values from the ten leave one out cross validation experiments are shown in the table. The execution times, rather than the number of iterations are listed. Since the experiments were run on the same code, using Matlab, this still provides a fair comparison of the measure of complexity caused by the varying HUs.  Table 1: NN classification results  MLP-BP HU Training time (s) Testing time (s) Accuracy (%) 20 105.08 0.0148 94.55 40 151.63 0.0157 91.90 60 181.24 0.0157 89.75 80 220.39 0.0158 91.63 100 255.41 0.0159 87.75 Average 182.75 0.0156 91.12  The best classification accuracy of 94.55% was obtained for HU size of 20. Classification experiments were also tried with HUs lower than 20 but their performances were poor as the MLP-BP failed to converge and hence not reported here. The lower accuracies when using higher HUs could most probably be due to the overfitting phenomena. The training and testing times were also the shortest using 20 HUs. As such, it would be appropriate to use this HU size as the design complexity and computational time would be reduced in addition to giving the best classification accuracy.  4. DISCUSSION Gamma band oscillations are evoked during visual perception especially when a stimulus is recognised and these oscillations contribute to feature binding process which is necessary during stimulus perception [3]. Another study [6] speculated that the function of gamma band oscillations is to provide a reference clock to control the firing of the excitatory neurons.  It is likely that long term use of alcohol causes irreversible damage to neuronal excitations leading to the difference in gamma band energies that discriminate alcoholics from non-alcoholics. Though the t-test gives significant difference between alcoholics and non-alcoholics for different gamma band energies for a few common channels; the significant difference for different channels for different gamma band energies show that electrophysiological deficits related to perception and gamma band oscillations occur differently at different areas of the brain.   5. CONCLUSION In this paper, a method to discriminate alcoholic subjects is proposed using energy of multiple gamma bands of EEG signals. The recording procedure is simple as the EEG is recorded when subjects perceive a single picture. MLP-BP NN classifies the EEG features from 3 gamma band energies (20-30 Hz, 30-50 Hz, 50-70 Hz) from selected channels into either the alcoholics or non-alcoholics category. The good discrimination accuracy obtained in this study offers promise for the method to be developed further for use in alcoholic screening tests.   6. ACKNOWLEDGEMENTS The authors acknowledge the assistance of the late Prof. H. Begleiter of Neurodynamics Laboratory, State of New York, USA who recorded the raw EEG data and Mr. P. Conlon of SascoHill Research for sending the data.  7. REFERENCES [1] M. Aalto, K. Seppä, K. Kiianmaa, and P. Sillanaukee, “Drinking habits and prevalence of heavy drinking among primary health care outpatients and general population”, Addiction, Vol. 94, 1999, pp.1371-1379. [2] L. Almasy, B. Porjesz, J. Blangeo, A. Goate, H.J. Edenberg, D.B. Chorlian, S. Kuperman, S.J. O’Connor, J. Rohrbaugh, L.O. Bauer, T. Foroud, J.P. Rice, and T. Reich, “Genetics of event-related brain potentials in response to a semantic priming paradigm in families with a JCS&T Vol. 7 No. 2                                                                                                                                 April 2007184history of alcoholism”, American Journal of Human Genetics, Vol. 68, 2001, pp. 128-135. [3] E. Basar, Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory. Boca Raton: CRC Press, 2004. [4] H. Begleiter, B. Porjesz, and W. Wang, A Neurophysiologic Correlate of Visual Short-term Memory in Humans. Electroencephalography and Clinical Neurophysiology, Vol. 87, 1993, pp. 46-53. [5] H. Begleiter, B. Porjesz, T. Reich, H.J. Edenberg, A. Goate, J. Blangero, L. Almasy, T. Foroud, P. V. Eerdewegh, J. Polich, J. Rohrbaugh, S. Kuperman, L.O. Bauer, S.J. O’Connor, D.B. Chorlian, T.K. Li, P.M. Conneally, V. Hesselbrock, J.P. Rice, M.A. Schuckit, R. Cloninger, J. Nurnberger Jr., R. Crowe, and F.E. Bloom, “ Quantitative Trait Loci Analysis of Human Event-related Brain Potentials”, Electroencephalography and Clinical Neurophysiology, Vol. 108, 1998, pp. 244-250.  [6] J. G. R. Jefferys, R.D. Traub, and M.A. Whittington, “ Neuronal networks for induced “ 40 Hz” rhythms”, Trends in Neurosciences, Vol. 19, 1996, pp.202-208. [7] K. E. Misulis, Spehlmann’s Evoked Potential Primer: Visual, Auditory and Somatosensory Evoked Potentials in Clinical Diagnosis, Butterworth-Heinemann, 1994. [8] R. Palaniappan, P. Raveendran, and S. Omatu, “ VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics”, IEEE Transactions on Neural Network, Vol. 13, No. 2, 2002, pp.486-491. [9] J.G. Snodgrass and M. Vanderwart, “ 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, 1980, pp.174-215. [10] X. L. Zhang, H. Begleiter, B. Porjesz, and A. Litke, “ Electrophysiological Evidence of Memory Impairment in Alcoholic Patients”, Biological Psychiatry, Vol.42, 1997, pp.1157-1171.  JCS&T Vol. 7 No. 2                                                                                                                                 April 2007185

English

Screening for chronic alcoholic subjects using multiple gamma band EEG: a pilot study

Servicio de Difusión de la Creación Intelectual

SEDICI - Repositorio de la UNLP

Screening for Chronic Alcoholic Subjects Using Multiple Gamma Band 
EEG: A Pilot Study 
 
Ramaswamy Palaniappan  
Department of Computer Science, University of Essex 
Wivenhoe Park, CO4 3SQ, Colchester, United Kingdom 
Email: rpalan@essex.ac.uk 
 
ABSTRACT 
Electrophysiological impairments of alcoholism 
have been researched extensively. However, there is 
none or few reported research on screening methods 
for chronic alcoholic subjects. Since chronic 
alcoholics have serious brain dysfunction, a method 
to screen for them during specific job applications 
that require good memory, concentration and/or 
decision making would be useful. In this paper, a 
method is proposed to discriminate chronic alcoholic 
from non-alcoholic subjects while they are sober. 
Energies of electroencephalogram signals in 
multiple gamma bands recorded while the subjects 
performed a picture recognition task are used as 
features by a neural network to detect the chronic 
alcoholic subjects. Leave one out cross validation 
strategy reveals that alcoholics could be 
discriminated from non-alcoholics with accuracy of 
94.55%. This pilot study has shown the potential of 
the method which could be further developed for use 
in automatic alcoholic screening procedures.  
Keywords: Alcoholic subject discrimination, 
Electroencephalogram, Gamma band energy, Neural 
network 
 
1. INTRODUCTION 
Alcohol abuse is associated with many health and 
social problems and brings high economic costs. It is 
estimated that 9–20% of patients admitted to a 
primary healthcare service are screened positive for 
alcohol abuse [1]. Analyses on the brain dysfunction 
caused by alcoholism have been researched 
extensively. Chronic alcoholics, who have been 
drinking heavily for many years, have been shown to 
have problems with memory, attention and decision 
making even after a period of abstinence [2], [4], 
[10]. Therefore, screening for alcoholics could be of 
importance during certain job recruitments that need 
some of these skills (like machine operators 
requiring concentration).  
However, there are not many reported studies on 
methods for detecting chronic alcoholics from the 
rest of the population when they are sober. This 
must not be confused with methods for detecting 
alcoholics under the influence of alcohol (using 
alcohol levels in blood, for example like the breath 
analyser test). The only related study to the one here 
was that by [8] who investigated a method to select 
optimal channels using genetic algorithm, where 
they particularly studied a two class problem using 
groups of alcoholics and non-alcoholics. Both the 
alcoholics and non-alcoholics were included in the 
training and testing data sets in this study and their 
objective was not on the individual discrimination of 
alcoholic subjects.  
In this paper, an attempt is made to discriminate 
chronic alcoholic subjects from non-alcoholic 
subjects using features from brain signals. The 
validity of the method is shown through an analysis 
involving several stages of pre-processing, feature 
extraction and classification methodologies.  
 
2. METHODOLOGY 
In this study, electroencephalogram (EEG) data from 
ten alcoholics and ten non-alcoholics were used. 
This data set were recorded by the authors in [10], 
for their studies on establishing a marker to analyse 
the short-term visual memory deficits in alcoholics.  
   
The subjects 
The alcoholics tested had been abstinent for a 
minimum period of one month (through closed ward 
detention). Therefore, all alcoholics were fully 
detoxified and had no alcohol available for that 
period of hospitalisation. Alcoholic individuals were 
excluded from the study if they had history of drug 
dependence, major psychiatric illness, or other 
diseases related to overt liver, metabolic, vascular 
and neurological. Most of the alcoholics had been 
drinking heavily for a minimum of 15 years. The 
diagnosis of alcohol abuse was made by the intake 
psychiatrist of the Addictive Disease Hospital in 
Brooklyn, USA according to DSM-III criteria. The 
alcoholics were non-amnesics and also not substance 
abusers. They were also matched for socioeconomic 
status.  
 
EEG data 
The EEG signals were recorded non-invasively from 
61 active plus 3 reference channels from the scalp. 
The positions of 19 electrodes were in conjunction 
with the 10-20 international system and additional 
electrodes were placed in between these to obtain 
the total of 61 electrodes, covering most of the 
recordable activity of the brain. Figure 1 shows the 
location of these electrodes.  
 
JCS&T Vol. 7 No. 2                                                                                                                                 April 2007
182
FP1
T8
AF7
F7
FC7
OZO1
TP7
T5
P07
CZ
CPZ
PZ
POZ
AFZ
FZ
FCZ
FP2
T10
AF8
F8
FC8
O2
TP8
T6
PO8
C5 C3 C1 C2 C4 C6
FC5 FC3 FC1
F5 F3 F1
AF1
FC2 FC4 FC6
F2
AF2
F6F4
CP2 CP4 CP6
P2 P4 P6
PO4PO3
CP1CP3CP5
P1P3P5
FPZ
 
 
Figure 1. 61 active electrode system. 
 
EEG signals are actually potentials exhibited by 
neuronal excitations in the cortex and these were 
recorded with the subjects observing drawings of 
common black and white objects from the Snodgrass 
and Vanderwart (SV) picture set [9]. The SV picture 
set represent common black and white objects, such 
as, for instance, airplane, banana, and ball. These 
were chosen according to a set of rules that provides 
consistency of pictorial contents. They have been 
standardised based on the variables of central 
relevance to memory and cognitive processing. 
These objects can be named i.e. they have definite 
verbal labels.  
All the subjects were asked to remember or 
recognise the stimulus. Stimulus duration of every 
picture was 300 ms. All the stimuli were shown 
using a display located 1 meter away from the 
subjects. One-second EEG measurements (sampled 
at 256 Hz) after each stimulus onset were stored. 
Figure 2 shows some examples of the picture 
stimuli. 
 
 
 
 
 
 
Figure 2. Examples of stimuli from SV set [9]. 
 
Pre-processing 
EEG signals contaminated with eye blink artifacts 
were not considered in the approach, and were 
detected using a 100 PV threshold. This is a 
common threshold value in EEG studies, and is used 
since blinking produces 100-200 PV potential 
lasting 250 milliseconds [7]. A total of 40 artifact 
free trials were considered for every subject, to make 
a total 800 EEG signals. 
 
 
 
Feature extraction 
EEG signals were filtered using Elliptic filter. 
Filtering was performed twice; once forward and 
once reverse to remove the non-linear phase effects. 
Frequency range was from 20-70 Hz because of the 
reported existence of gamma band oscillations in 
this range [3].  
This frequency range was split into 3 gamma bands: 
low-gamma (20-30 Hz), mid-gamma (30-50 Hz) and 
high-gamma (50-70 Hz). Order 5 was sufficient to 
obtain pass-bands of 20-30 Hz, 30-50 Hz and 50-70 
Hz with minimum stop-band attenuation of 30 dB at 
5 Hz below and above the pass-band ranges. The 
ripple in the pass-bands was kept below 0.1 dB. 
Energy of the filtered EEG signal in each low-
gamma, mid-gamma and high-gamma bands from 
each channel were computed and normalised with 
the total energy in each band from 61 channels to 
form a total of 183 EEG features. 
To reduce the number of features, a simple 
technique using t-test1 analyses (with significance of 
0.01) were used to obtain the most discriminant 
channels for each band. For each gamma band, the 
channels that gave significant difference between 
alcoholic features and non-alcoholic features were 
noted and only features from these channels for the 
particular gamma band were used in classification. 
There were 33 significant channels for low-gamma, 
36 significant channels for mid-gamma and 54 
significant channels for high-gamma, giving a total 
of 123 features for each EEG signal. There were 
only 21 common significant channels for the 3 
gamma bands, the rest of the significant channels 
differed.  
Neural network classification  
Features from nine alcoholics and nine non-
alcoholics (totalling 720 feature set, each with 123 
features) were used in training the multilayer-
perceptron (MLP) neural network (NN), while the 
rest of the features from the other alcoholic and 
other non-alcoholic (totalling 80 feature set) were 
used in the testing. The feature sets used in training 
and testing were whitened to remove correlation.  
Signal whitening seeks to obtain the EEG matrix x~ , 
such that the covariance of matrix x~  becomes the 
identity matrix 
 
I)xxE( T  ~~ . (1) 
 
A common whitening method is via the eigenvalue 
decomposition of the covariance matrix 
TT FDF)E(xx  , where F  is the orthogonal matrix 
of eigenvectors of )E(xxT  and D  is the diagonal 
matrix of its 
eigenvalues, ).,....,( 1 ndddiag D Whitening can now 
be performed using  
                                                 
1
 The features were assumed to be of normal distribution. 
JCS&T Vol. 7 No. 2                                                                                                                                 April 2007
183
 xFFDx T1/2  ~ . (2) 
 
These whitened features were then normalised to the 
range [-1,1], using maximum and minimum values 
of each feature, with the idea to improve the NN 
training; 
MLP NN was trained by the backpropagation 
algorithm (BP) with adaptive learning rate to 
classify the features. The hidden units (HU) were 
varied from 20 to 100 in steps of 20. The number of 
input units was set at 123. The output units were set 
at two: one for each alcoholic or non-alcoholic 
category. One-hot encoding was used for the target 
values (either 0 or 1). The training was conducted 
until the mean-square error fell below a threshold of 
0.01. The MLP-BP training and testing procedures 
were repeated for ten times but with training and 
testing feature sets from different alcoholics and 
different non-alcoholics for each time. In other 
words, feature sets from different nine alcoholics 
and nine different non-alcoholics were used for 
training, while feature sets from the other alcoholic 
and other non-alcoholic were used for testing each 
time. This procedure of repeating the training and 
testing is commonly referred as the leave one out 
cross validation, which is useful in increasing the 
reliability of the classification results. 
 
3. RESULTS 
Table 1 shows the MLP-BP NN training, testing 
times and the classification accuracies for varying 
HU sizes. Only the averaged values from the ten 
leave one out cross validation experiments are 
shown in the table. The execution times, rather than 
the number of iterations are listed. Since the 
experiments were run on the same code, using 
Matlab, this still provides a fair comparison of the 
measure of complexity caused by the varying HUs. 
 
Table 1: NN classification results 
 
MLP-BP HU Training 
time (s) 
Testing 
time (s) 
Accuracy 
(%) 
20 105.08 0.0148 94.55 
40 151.63 0.0157 91.90 
60 181.24 0.0157 89.75 
80 220.39 0.0158 91.63 
100 255.41 0.0159 87.75 
Average 182.75 0.0156 91.12 
 
The best classification accuracy of 94.55% was 
obtained for HU size of 20. Classification 
experiments were also tried with HUs lower than 20 
but their performances were poor as the MLP-BP 
failed to converge and hence not reported here. The 
lower accuracies when using higher HUs could most 
probably be due to the overfitting phenomena. The 
training and testing times were also the shortest 
using 20 HUs. As such, it would be appropriate to 
use this HU size as the design complexity and 
computational time would be reduced in addition to 
giving the best classification accuracy. 
 
4. DISCUSSION 
Gamma band oscillations are evoked during visual 
perception especially when a stimulus is recognised 
and these oscillations contribute to feature binding 
process which is necessary during stimulus 
perception [3]. Another study [6] speculated that the 
function of gamma band oscillations is to provide a 
reference clock to control the firing of the excitatory 
neurons.  
It is likely that long term use of alcohol causes 
irreversible damage to neuronal excitations leading 
to the difference in gamma band energies that 
discriminate alcoholics from non-alcoholics. Though 
the t-test gives significant difference between 
alcoholics and non-alcoholics for different gamma 
band energies for a few common channels; the 
significant difference for different channels for 
different gamma band energies show that 
electrophysiological deficits related to perception 
and gamma band oscillations occur differently at 
different areas of the brain.  
 
5. CONCLUSION 
In this paper, a method to discriminate alcoholic 
subjects is proposed using energy of multiple 
gamma bands of EEG signals. The recording 
procedure is simple as the EEG is recorded when 
subjects perceive a single picture. MLP-BP NN 
classifies the EEG features from 3 gamma band 
energies (20-30 Hz, 30-50 Hz, 50-70 Hz) from 
selected channels into either the alcoholics or non-
alcoholics category. The good discrimination 
accuracy obtained in this study offers promise for 
the method to be developed further for use in 
alcoholic screening tests.  
 
6. ACKNOWLEDGEMENTS 
The authors acknowledge the assistance of the late 
Prof. H. Begleiter of Neurodynamics Laboratory, 
State of New York, USA who recorded the raw EEG 
data and Mr. P. Conlon of SascoHill Research for 
sending the data. 
 
7. REFERENCES 
[1] M. Aalto, K. Seppä, K. Kiianmaa, and P. 
Sillanaukee, “Drinking habits and prevalence of 
heavy drinking among primary health care 
outpatients and general population”, Addiction, 
Vol. 94, 1999, pp.1371-1379. 
[2] L. Almasy, B. Porjesz, J. Blangeo, A. Goate, 
H.J. Edenberg, D.B. Chorlian, S. Kuperman, 
S.J. O’Connor, J. Rohrbaugh, L.O. Bauer, T. 
Foroud, J.P. Rice, and T. Reich, “Genetics of 
event-related brain potentials in response to a 
semantic priming paradigm in families with a 
JCS&T Vol. 7 No. 2                                                                                                                                 April 2007
184
history of alcoholism”, American Journal of 
Human Genetics, Vol. 68, 2001, pp. 128-135. 
[3] E. Basar, Memory and Brain Dynamics: 
Oscillations Integrating Attention, Perception, 
Learning, and Memory. Boca Raton: CRC 
Press, 2004. 
[4] H. Begleiter, B. Porjesz, and W. Wang, A 
Neurophysiologic Correlate of Visual Short-
term Memory in Humans. 
Electroencephalography and Clinical 
Neurophysiology, Vol. 87, 1993, pp. 46-53. 
[5] H. Begleiter, B. Porjesz, T. Reich, H.J. 
Edenberg, A. Goate, J. Blangero, L. Almasy, T. 
Foroud, P. V. Eerdewegh, J. Polich, J. 
Rohrbaugh, S. Kuperman, L.O. Bauer, S.J. 
O’Connor, D.B. Chorlian, T.K. Li, P.M. 
Conneally, V. Hesselbrock, J.P. Rice, M.A. 
Schuckit, R. Cloninger, J. Nurnberger Jr., R. 
Crowe, and F.E. Bloom, “ Quantitative Trait 
Loci Analysis of Human Event-related Brain 
Potentials”, Electroencephalography and 
Clinical Neurophysiology, Vol. 108, 1998, pp. 
244-250.  
[6] J. G. R. Jefferys, R.D. Traub, and M.A. 
Whittington, “ Neuronal networks for induced 
“ 40 Hz” rhythms”, Trends in Neurosciences, 
Vol. 19, 1996, pp.202-208. 
[7] K. E. Misulis, Spehlmann’s Evoked Potential 
Primer: Visual, Auditory and Somatosensory 
Evoked Potentials in Clinical Diagnosis, 
Butterworth-Heinemann, 1994. 
[8] R. Palaniappan, P. Raveendran, and S. Omatu, 
“ VEP optimal channel selection using genetic 
algorithm for neural network classification of 
alcoholics”, IEEE Transactions on Neural 
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Electrophysiological impairments of alcoholism have been researched extensively. However, there is none or few reported research on screening methods for chronic alcoholic subjects. Since chronic alcoholics have serious brain dysfunction, a method to screen for them during specific job applications that require good memory, concentration and/or decision making would be useful. In this paper, a method is proposed to discriminate chronic alcoholic from non-alcoholic subjects while they are sober. Energies of electroencephalogram signals in multiple gamma bands recorded while the subjects performed a picture recognition task are used as features by a neural network to detect the chronic alcoholic subjects. Leave one out cross validation strategy reveals that alcoholics could be discriminated from non-alcoholics with accuracy of 94.55%. This pilot study has shown the potential of the method which could be further developed for use in automatic alcoholic screening procedures

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Screening for chronic alcoholic subjects using multiple gamma band EEG: a pilot study

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