Chaotic features analysis of EEG signals during hallucination tasks of waterloo-stanford standard

doi:10.4236/jbise.2010.312153

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J. Biomedical Science and Engineering, 2010, 3, 1175-1181

doi:10.4236/jbise.2010.312153 Published Online December 2010 (http://www.SciRP.org/journal/jbise/ JBiSE

Published Online December 2010 in SciRes. http://www.scirp.org/journal/JBiSE

Chaotic features analysis of EEG signals during hallucination

tasks of waterloo-stanford standard

Elahe’ Yargholi1, Ali Motie Nasrabadi2

1Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran;

2Biomedical Engineering, Shahed University, Tehran, Iran.

Email: [email protected]; [email protected]

Received 9 October 2010; revised 15 October 2010; accepted 20 October, 2010

ABSTRACT

The present study looks carefully at EEG (Electroen-

cephalograph) signals of people after the hypnosis

inductions. Subjects were in three different categories

of hypnotizability based on Waterloo-Stanford crite-

ria; low, medium and high. Signals recorded during

hallucination tasks of Waterloo-Stanford standard

were applied to study the underlying dynamics of

tasks and investigate the influence of hypnosis depth

and concentration on recorded signals. To fulfill this

objective, chaotic methods were employed; Higuchi

dimension and correlation dimension. The results of

the study indicate channels whose chaotic features

are significantly different among people with various

levels of hypnotizability. Moreover, a great consis-

tency exists among channels involved in each task

with brain’s dominant hemisphere and brain lobes’

functions. Another considerable result of the study

was that the medium hypnotizable subjects were

mostly affected by inductions and instructions of the

hypnotizer (more than low or high hypnotizable sub-

jects). The present study demonstrates a remarkable

innovation in the analysis of hypnotic EEG; investi-

gating the EEG signals of the hypnotized as doing

hallucination tasks of Waterloo-Stanford standard

orders.

Keywords: Hypnosis; Hypnotizability;

Waterloo-Stanford Standard; Fractal Dimension

1. INTRODUCTION

To define hypno sis, it could be referred to a mental state

produced through induction. Some people think of that

as a kind of hypnoidal anesthesia while the various neu-

rological researches reveal that hypnosis is a state of

consciousness in which the individual enjoys the high

degree of concentration [1] and as being isolated from

peripheral environment, s/he is extremely suggestible.

For the time being, hypnosis is applied in various

fields such as medicine, psychology, dentistry and …. To

make use of hypnosis as a therapeutic means, the patient

should be appropriately receptive to hypnosis to get the

required depth-point. It is the point at which the patient

would take and accept the therapeutic instructions and

could behave or act based on received inductions so that

the therapy could be efficiently carried out. International

standards are applied to assess/estimate the depth of

hypnosis. According to the international standards, the

individual goes under hypnosis, then the hypnotizer or-

ders her/him to do a special task. How the patient fol-

lows the order helps the hypnotizer to estimate the depth.

This method may cause a hypnosis-depth decrease, so it

is more beneficial to apply methods which estimate the

hypnosis depth ordering the patient and this is what a

great deal of researches aim at.

In recent years due to advances in the field of bio-

medical science, a large number of researches on hypno-

sis and its impact upon different biosignals have been

carried out. While under the influence of hypnosis, the

body experiences physiological shifts such as the change

in the rhythm of the heart, hypotension, resistance of

peripheral vessels, electrical resistance of the skin, basic

metabolism, body temperature, rate and depth of breath-

ing et al. Hypnotic inductions can also change the tone

of muscles and release of endocrine glands so it has led

scholars to do various researches on hypnosis, applying

EEG (Electroencephalograph), fMRI, PET, skin-resis-

tance measurement, heart rate et al. since EEG signal

recording, in comparison with other methods, is more

readily accessible and easier to use, a lot of experts have

applied it to hypnosis investigations.

A wide variety of studies focused on the spectrum of

the hypnotic EEG signals [1-17] while some recent re-

searches were performed not applying the spectrum.

Faber et al. studied the EEG signals during hand rising

in both not hypnotized and hypnotized states [18]. Nas-

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181

1176

rabadi studied EEG signals in different mental status

(baseline, tasks, hypnosis) in people with different hyp-

notizability [19]. Lee et al. applied fractal analysis to

investigate EEG signals in both states of hypnotized and

not hypnotized [20]. Solhjoo et al extracted fractal di-

mensions of normal and hypnotic EEG signal to classify

different mental tasks [21]. Baghdadi studied features

extracted from hypnotic EEG using improved empirical

mode decomposition (EMD) algorithm [22,23]. Ray

investigated the difference between EEG signals’ fractal

dimensions of lowly and highly hypnotizable subjects

[2]. Behbahani analyzed the nature of hypnosis in right,

left, back and frontal hemisphere in three groups of

hypnotizable subjects by means of fuzzy similarity index

method [24 ,25].

In researches carried out so far, different methods

have been used to examine EEG signals of the hypno-

tized subjects; However, most those researches applied

the EEG signals during induction and investigating the

EEG signals of the hypnotized as doing mental tasks

(standard orders of Waterloo-Stanford) has not still been

realized. Therefore in the pursuit of this purpose, the

current study has been proposed. This study examined

the recorded EEG signals of three groups of people

whose hypnotizability leve ls ranged in low, medium and

high hypnosis receptivity. The signal recording was done

as they were doing the mental tasks (standard orders of

Waterloo-Stanford) so, the impacts of hypnosis depth

and concentration rate on recorded signals of these three

groups of people -with different hypnotizability levels-

could be properly examined and variation of dynamics

throughout di fferent mental tasks would be investigat ed.

The researches performed in the last 25 years indi-

cates the existence of chaotic dynamics in both micro-

scopic (neuron performance) and macroscopic levels

(brain activities during sleep) [26,27], so obtaining the

accurate and better results, through the application of

chaotic methods to EEG investigation in the hypnotized

could be expected. By the means of extracting and com-

paring the chaotic features, the difference between the

recorded EEG throughout the same activities but differ-

ent hypnotizability depth-levels (low, medium and high)

could be examined. Therefore in th is research, the exist-

ing difference of the dominant dynamics in these three

groups with different hypnotizability an d also the kind of

differences have been studied. Should any distinction of

the extracted chaotic features between these three hyp-

notized groups observed, those differences could serve

as the criteria for hypnosis-depth determination to exert

appropriate inductions, EEG examination during hypno-

sis provides the data for studying hypnosis stages and

the transfer from one stage to another.

2. MATERIALS

For the required data in the study, Nasrabadi data base

[19] used. The data were obtained according to 20-10

standard consisting of 19 electrodes. The subjects of the

study included 33 male participants with age range of 32

± 6. The sampling was taken at 256 Hz. The subjects all

featured in left-hemisphere dominancy. Being right-

handed (writing with right hand) was the criterion to

recognize the subjects as left-hemisphere dominants.

The signal recording time was the same for all (16

pm-20 pm). Signal recording were taken twice and in 2

different situations: once in a state of being relaxed with

their eyes closed-baseline signal- and for the second time,

the recording was taken at the state of being hypnotized.

To examine the shifts in the level of hypnosis, EEG sig-

nals are required to be recorded under the state of hyp-

nosis. To do so, following the whole stages of Water-

loo-Stanford standard, a 45-minute audio file was pro-

vided, the same audio file was utilized for hypnosis in-

duction in all the subjects and there was no change in

speech tone. So, all the subjects were placed under the

equal circumstances. The first 15 minutes of audio file

was assigned to the hypnosis induction. It starts includ-

ing an individual- conscious and in normal state, then in

order to determine the hypnotizability score, the partici-

pant is asked to do 12 different tasks as the following

respectively:

1) Hand lowering (ideomotors),

2) Moving hands together (ideomotors),

3) Experience of mosquito (hallucination),

4) Taste experience (hallucination),

5) Arm rigidity (challenge),

6) Dream (memory),

7) Arm immobilization (challenge),

8) Age regressi on (mem ory),

9) Music hallucination ( hall u ci nati on),

10) Negative vi sual (hall ucination),

11) Posthypnotic automatic writing (memory) and

12) Amnesia (memory).

When tasks-performance ends, the scores of hypno-

tizability and the depth of hypnosis are de termined based

on each individual performance [28,29]. The EEG sig-

nals of all electrodes have been recorded throughout

induction and all tasks. In present research EEG signals

of hallucination were chosen to investigate.

3. METHOD

Parameters that represent chaotic behavior may be di-

vided to two categories. The first category indicates dy-

namic behavior. Maximum Lyapunov Exponent (MLE)

is of this category. These parameters state how the sys-

tem behaves oh the nearby trajectories. The second cat-

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181 1177

egory emphasizes the geometric property of basin of

attraction. Fractal dimension is of this category [21]. In

present study two fractal dimensions were used: Higuchi

fractal dimension and correlation dimension.

3.1. Higuchi Algorithm

k new time series are constructed from the signal under

study:

  

,,2,,

for 1,2,,

mNm

xm xm kxmkxmk



























(1).

Where m and k indicate the initial time value, and the

discrete time interval between points, respectively. For

each of the k time series k

, the length of





Lk is

computed:

  



111

xm ikxmikN

Lk Nm

 









 (2).

Where N is the to tal length of the signal x(1), x(2), …,

x(N). An average length is computed as the mean of the

k lengths (for m = 1,2,…,k ). This procedure is

repeated for each k ranging from 1 to max , obtaining an

average length for each k. Then the slope of th e best fit-

ted line to the curve of versus



Lk k







In Lk





In1 k is

the estimate of Higuchi fractal dimension [30].

3.2. Correlation Dimension

It begins by writing the correlation sum as the following

form:



1ij

dij

CR Rxx

NN 











(3).

Where d is the number of embedding dimension and x

values are vectors in that embedding dimension. Then

the correlation sum tells us the relative number of pair of

points that are located within the distance of R of each

other in this space. is defined to be the number

that satisfies



 

dRCRk



. For

dd (a satu-

ration value), becomes independent of the embed-

ding dimension d and this is the estimate of correlation

dimension (Figure 1) [31-33]. A d dimensional vector is

the collection of d components





,, ,,

LL L

iiitit id t

xxxxx

 









(4).

Where

t is called the time lag and represents the

time interval between the successively sampled values

that we use to construct the vector i

. To choose the

time lag

t there are two dominant methods. The first

is to choose the lag at which the first zero-crossing of the

Figure 1. as a function of embedding dimension.

autocorrelation function for the data occurs. Another

method is to select the first local minimum of the aver-

age mutual information function. In present study the

later procedure was applied [34].

4. RESULTS

First, the DC parts of each signal were removed and Hi-

guchi and correlation dimension of both baseline and

hypnotic signals calculated. In the next stage, with the

purpose of normalizing the fractal dimensions of hyp-

notic signals, those of baseline signals were employed.

Applying ANOVA statistic analysis, the following stage

went on in order to find out if there is a significant dif-

ference of their features- either normalized or not nor-

malized- among three hypnotizable groups (low: 1, me-

dium: 2 and high: 3) while going through the same tasks.

Analysis results of hallucination tasks including chan-

nels whose fractal dimension enable us to differentiate

three hypnotizable groups with p-values less than 0.05

and mean values of fractal dimensions for distinguished

groups are presented in the following tables.

As can be seen from Ta bles 1 and 2 of task3 (experi-

ence of mosquito), Tables 3 and 4 of task4 (taste ex-

perience), Tables 5 to 8 of task9 (music hallucination)

and Table s 9 to 12 of task10 (negative visual), in hallu-

cination tasks, channels of left hemisphere (except

task3)and frontal lobe were more efficient and this is

consistent with function of frontal lobe concerned with

the reception and processing of sensory information

from the body [35].

5. CONCLUSIONS

Employing the chaotic methods, the current study was

going to find out the impact, if any, of hypnosis depth on

EEG signals recorded while the individuals were doing

mental tasks under the hypnosis. The results of the study

indicates just a few number of channels, not all, can be

of an aid in discriminating between people with various

levels of hypnotizability and the similarity among those

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181

1178

Table 1. Task3-Higuchi dimension-Normalized.

channels Distinguished groups(mean)

1a(0.963) 3(1.060)

a. Low: 1, medium: 2, high : 3 .

Table 2. Task3-Correlation dimension-Normalized.

channels Distinguished groups(mean)

2(5.2483) 3(3.074)

1(5.099) 3(2.051)

2(4.073) 3(2.051)

Table 3. Task4-Higuchi dimension-Normalized.

channels Distinguished groups(mean)

1(0.957) 2(1.009)

1(0.958) 3(1.079)

2(1.019) 3(1.079)

1(0.961) 3(1.071)

2(0.998) 3(1.071)

C 1(0.981) 3(1.054)

P 2(0.995) 3(1.041)

Table 4. Task4-Correlation dimension-Not normalized.

channels Distinguished groups(mean)

C 2(8.398) 3(6.758)

Table 5. Task9-Higuchi dimension-Not normalized.

channels Distinguished groups(mean)

P 1(0.736) 2(0.858)

channels of the same task type is considerable (Figure

2).

Looking closely at the results, it can be noticed that in

all tasks except task3, channels of left hemisphere were

more efficient and this fact may be due to subjects being

right-handed and left hemisphere dominancy.

A great consistency exists between channels involved

with corresponding brain lobes: task3, task4, task9 and

task10 of hallucination type; channels of frontal lobe.

A remarkable finding yielded through the statistic in

Table 6. Task9-Higuchi dimension-Normalized.

channels Distinguished groups(mean)

1(0.914) 2(1.0199)

1(0.914) 3(1.063)

1(0.906) 3(1.092)

2(1.020) 3(1.092)

1(0.964) 3(1.084)

2(1.020) 3(1.084)

1(0.972) 3(1.069)

C 2(1.013) 3(1.069)

1(0.908) 2(1.015)

1(0.908) 3(1.094)

2(1.015) 3(1.094)

1(0.875) 2(0.997)

1(0.875) 3(1.056)

2(0.997) 3(1.056)

Table 7. Task9-Correlation dimension-Not normalized.

channels Distinguished groups(mean)

C 1(5.893) 2(8.543)

1(2.275) 2(7.661)

1(2.275) 3(7.160)

1(4.212) 2(7.628)

1(4.212) 3(7.590)

O 1(3.842) 2(8.967)

Table 8. Task9-Correlation dimension-Normalized.

channels Distinguished groups(mean)

T 1(1.562) 2(4.549)

Table 9. Task10-Higuchi dimension-Not normalized.

channels Distinguished groups(mean)

P 1(0.715) 2(0.854)

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181

1179

Figure 2. channels of hallucination tasks.

Table 10. Task10-Higuchi dimension-Normalized.

channels Distinguished groups(mean)

1(0.910) 2(1.004)

1(0.894) 3(1.070)

2(1.000) 3(1.070)

1(0.939) 3(1.052)

1(0.850) 2(0.992)

P 1(0.850) 3(1.030)

vestigations was that in ex tracting various features of all

10 tasks and all 19 channels the features’ variance of the

medium hypnotizable group was the least and the low

hypnotizable group showed the maximum variance in

each extracted feature. This fact shows that the medium

hypnotizable subjects were mostly affected by induc-

tions and instructions of the hypnotizer (more than low

or high hypnotizable subjects) and the low hypnotizable

subjects had the least affectability while the high hypno-

tizable subjects stood in between, less affectability than

medium hypnotizables and more affectability than low

hypnotizable ones.

For further study these channels could be applied to

proposing various kinds of classifiers so as to define

more objective hypnosis scoring methods.

Moreover, the results of the study can be applied to

study the dynamics of any task by the means of fractal

dimensions.

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181

1180

Table 11. Task10-Correlation dimension-Not normalized.

channels Distinguished groups(mean)

1(3.666) 2(8.050)

1(3.666) 3(7.096)

1(5.039) 2(8.423)

1(3.510) 2(7.683)

1(3.510) 3(7.532)

1(4.763) 2(7.546)

C 1(5.117) 2(8.374)

1(4.838) 2(8.534)

C 1(4.838) 3(8.348)

C 1(5.277) 2(8.976)

1(4.484) 2(8.260)

P 2(8.260) 3(6.454)

1(3.419) 2(8.842)

P 1(3.419) 3(8.022)

O 1(3.694) 2(8.608)

O 1(5.112) 2(9.186)

Table 12. Task10-Correlation dimension-Normalized.

channels Distinguished groups(mean)

1(1.310) 2(4.068)

1(1.310) 3(3.586)

C 1(2.305) 2(5.431)

P 2(5.077) 3(3.011)

Hallucination tasks:

1) In task3, correlation dimensions of group 3 are less

than others and by contrast higuchi dimension of group 3

is higher than that of group1.

2) In task4, Higuchi dimension of group 3 are greater

than others, Higuchi dimension of group1 is less than

that of group2 and by contrast correlation dimension of

group 2 is higher than that of group3.

3) Task9 indicates group 1 fractal dimensions are less

than those of group 2 and 3 and dimensions of group 3

are greater than those of group 2.

4) Task10 shows group 1 fractal dimensions are less

than those of group 2 and 3 and higuchi dimension of

group 3 is greater than those of group 2 while correlation

dimension of group 3 is less than those of group 2.

Considering the above-mentioned analysis, similari-

ties among dynamics of tasks of the same type are con-

siderable. The results of the study can not be compared

with any other research because there is no previous

study over EEG signal s o f Wate rl oo -Sta nf or d tasks.

REFERENCES

[1] Gruzelier, J.H. (1998) A working model of neurophysi-

ology of hypnotic relaxation. 5th Internet World Con-

gress for biomedical sciences. McMaster University,

Canada, INABIS 98.

http://www.mcmaster.ca/inabis98/woody/gruzelier0814/t

wo.html

[2] Ray, W.J. (1998) Understanding hypnosis and hypnotic

susceptibility from a psychophysiological perspective.

5th Internet Word Congress for Biomedical Sciences,

INABIS98.

http://www.mcmaster.ca/inabis98/woody/ray0556/index.

html

[3] De Pascalisa, V., Ray, W.J., Tranquilloa, I. and D’Amicoa,

D. (1998) EEG activity and heart rate during recall of

emotional events in hypnosis: relationships with Hypno-

tizability and suggestibility. International Journal of

Psychophysiology, 29, 255-275.

[4] Galbraith, G.C., et al. (1970) EEG and Hypnotic Suscep-

tibility. Journal of Comparative and Physiological Psy-

chology, 72, 125-131.

[5] Crawford, H.J. (1998) Brain dynamic shifts during the

elimination of perceived pain and distress: Neuroimaging

studies of hypnotic analgesia. 5th Internet Word Congress

for Biomedical Sciences, INABIS98.

http://www.mcmaster.ca/inabis98/woody/crawford0611/t

wo.html

[6] Crawford, H.J., et al. (1996) Self-Generated happy and

emotions in low and highly hypnotizable person during

waking and hypnosis: Laterality and regional EEG activ-

ity differences. International Journal of Psychophysiol-

ogy, 24, 239-266.

[7] De Pascalis, V. (1998) Brain mechanisms and attentio-

anal processes in hypnosis. 5th Internet Word Congress

for Biomedical Sciences, INABIS98.

[8] De Pascalis, V., et al. (1996) EEG asymmetry and heart

rate during experience of hypnotic analgesia in high and

low hypnotizables. International Journal of Psycho-

physiology, 21, 163-175.

[9] Graffin, N.F., Ray, W.J. and Lundy, R. (1995) EEG con-

comitants of hypnosis and hypnotic susceptibility. Jour-

nal of Abnormal Psychology, 104, 123-131.

[10] Williams, J. D. and Gruzelier, J. (2001) Differentiation of

hypnosis and relaxation by analysis of narrow band theta

and alpha frequencies. International Journal of Clinical

and Experimental Hypnosis, 49, 185-206.

[11] Abootalebi, V. (2000) Investigation of hypnosis on EEG

higher order spectra,” Master Thesis, Sharif University of

Technology.

[12] (2008) Professional Hypnosis DataBank, February.

E. Yar gholi et al. / J. Biomedical Science and Engineering 3 (2010) 1175-1181

1181

http://www.altor.org

[13] Lubar, J.F., Gordon, D.M., Harrist, R.S., et al. (1991)

EEG correlates of hypnotic susceptibility based upon fast

fourier power spectral analysis. Biofeedback and Self-

Regulation, 16, 75-80.

[14] Brady, B. and Stevens, L. (2000) Binaural-beat induced

theta EEG activity and hypnotic susceptibility, American

Journal of Clinical Hypnosis, 43, 53-69.

[15] Fingelkurts, A.A., Fingelkurts, A.A., Kallio, S. and Rev-

onsuo, A. (2007) Cortex functional connectivity as a

neurophysiological correlates of hypnosis: An EEG case

study. Neuropsychologia, 45, 1452-1462.

[16] Dumas, R.A. (2007) EEG Alpha-Hypnotizability correla-

tions: A review . Psychophysiology, 14, 431-438.

[17] White, D., Ciorciari, J., Carbis, C. and Liley, D. (2009)

EEG correlates of virtual reality hypnosis. International

Journal of Clinical and Experimental Hypnosis, 57,

94-116.

[18] Faber, P.L., Gianotti, L.R.R., Wohlgemuth, P. and Leh-

mann, D. (2000) Frequency domain EEG source loca-

tions during arm levitation under hypnosis: a pilot study.

ISBET 2000, Millennium,Frankfurt on the Main, Ger-

many.

[19] Nasrabadi, A.M. (2003) Quantitative and qualitative

evaluation of consciousness variation and depth of hyp-

nosis through intelligent processing of EEG signals. Ph.D.

Thesis, Amir Kabir University.

[20] Lee, J.S., et al. (2007) Fractal analysis of EEG in hypno-

sis and its relationship with hypnotizability,” Interna-

tional Journal of Clinical and Experimental Hypnosis, 55,

14-31.

[21] Solhjoo, S., Nasrabadi, A.M. and Golpayegani, M.R.H.

(2005) EEG-based mental task classification in hypno-

tized and normal subjects. Engineering in Medicine and

Biology 27th Annual Conference, Shanghai, China, Sep-

tember 1-4, 2041-2043.

[22] Baghdadi, G. (2008) Hypnosis depth determination, using

empirical mode decomposition. Master Thesis, Shahed

University.

[23] Baghdadi, G. and Nasrabadi, A.M. (2009) Estimating

final depth of hypnosis using extracted fractal features by

EMD algorithms. 17th Iranian Conference on Electrical

Engineering. Iran University of Science and Technology,

Tehran, Iran.

[24] Behbahani, S. (2008) Analysis of hypnotic EEG signals

using fuzzy si milarity in dex. Master Thesis, Isl amic Azad

University, Science and Research Branch.

[25] Behbahani, S. and Nasrabadi, A.M. (2009) Application of

fuzzy similarity index method in processing of hypnosis.

Journal of Biomedical Science and Engineering, 2,

359-362.

[26] Aihara, K. and Matsumuto, G. (1986) Chaotic oscilla-

tions and bifurcations in squid giant axon. chaos, Man-

chester University Press.

[27] Babloyantz, A., Salazar, J.M. and Nicolis, C. (1985)

Evidence of chaotic dynamics of brain activity during the

sleep cycle. Physics Letter A, 111, 152-156.

[28] Bowers, K.S. (1998) Waterloo-Stanford group scale of

hypnotic susceptibility, Form C: manual and response

booklet. International Journal of Clinical Hypnosis, 46,

250-268.

[29] Krisch, I., et al. (1998) Experimental scoring for the Wa-

terloo-Stanford group scale. International Journal of

Clinical Hypnosis, 46, 269-279.

[30] Nasrabadi, A.M., Hashemi Golpaygani, M.R., Khalil-

zadeh, M.A. and Sharifi, A. (2003) Comparision between

linear and nonlinear EEG signal processing during dif-

ferent mental activies. Scientific Journal of Amirkabir A,

55, 592-600.

[31] Hillborn, R.C. (2001) Chaos and nonlinear dynamics.

2nd Edition, Oxford University Press, 379-381.

[32] Grassberger, P. and Procaccia, I. (1983) Characterization

of strange attractors. Physical Review Letters, 50,

346-349.

[33] Grassberger, P. and Procaccia, I. (1983) Measuring the

strangeness of strange attractors. Physica D, 9, 189-208.

[34] Fraser, A.M. and Swinney, H.L. (1986) Independent co-

ordinates for strange attractors from mutual information,

Physical Review A, 33, 1134-1140.

[35] Netter, F.H. (1983) The Ciba collection of medical illus-

trations, Volume 1: Nervous System; Part 1: Anatomy

and Physiology. CIBA Limited, West Caldwell.