Effect of Hypnosis and Hypnotisability on Temporal Correlations of EEG Signals in Different Frequency Bands

Transcription

1 Effect of Hypnosis and Hypnotisability on Temporal Correlations of EEG Signals in Different Frequency Bands Golnaz Baghdadi Biomedical Engineering Department, Shahed University, Tehran, Iran Ali Motie Nasrabadi Biomedical Engineering Department, Shahed University, Tehran, Iran Abstract: In this paper, the changes of the temporal correlation along EEG signals are investigated after hypnosis induction in different hypnotisable groups and in various frequency bands. The scaling exponent that is calculated by detrended fluctuation analysis has been used in order to detect the temporal correlation of the hypnosis and normal EEG in different frequency bands. EEG decomposition is done by empirical mode decomposition method. Thirty-two healthy, right-handed men with different hypnotisability were contributed in this study. The statistical results show that hypnosis induction has different significant effects on temporal correlation of EEG signals in various frequency bands. Keywords: Hypnosis, DFA, EMD, Hilbert Transform, Temporal Correlation, 67

2 1. Introduction There are different studies which evidence that the amplitude of EEG oscillations in the human brain possesses long-range temporal correlations [1]-[6]. Long-range temporal correlations show that events in the past affect the development of the process in the future. In various mental conditions, controlling the neuronal dynamics is done by different properties of the neuronal networks that make differences in long-range temporal correlations of various mental conditions in the brain [7]. Detrended fluctuation analysis (DFA) is a scaling analysis method that provides a simple quantitative parameter to represent the correlation properties of a signal [8]. DFA permits the detection of long-range correlations embedded in non-stationary time series. Studies showed that long-range temporal correlations in EEG could be detected by DFA [2]. Lee, et al. (2007) analysed the trends of EEG signals in waking and hypnotic conditions, using DFA and showed that the temporal correlations of EEG signals in hypnotic condition are different from the waking condition. They also showed that the eye-roll sign was significantly negatively correlated with the scaling exponents, which are the results of DFA algorithm. In the present study, the relation between scaling exponents, hypnosis induction and hypnotisability has been investigated in different frequency bands of hypnosis EEG. Combination of empirical mode decomposition method and Hilbert transform was used to extract different frequency bands of EEG signal. This method is introduced by Huang et al. (1998) [9] for analysing nonlinear, non-stationary signals. The major advantage of the EMD is that the basis functions are derived from the signal itself. Hence, the analysis is adaptive, in contrast to the wavelet method where the basis functions are fixed [10]. In this paper, first the data is decomposed into the delta, theta, alpha, beta and gamma frequency bands. Secondly, the DFA algorithm is performed on these frequency bands in different brain channels and finally the effect of hypnosis and hypnotisability on temporal correlations is evaluated by statistical analyses. 2. Material and Methods 2.1. Subjects and EEG Signals The data includes EEG signals, which have been recorded from 32 right-handed men along hypnosis. The EEG signals have been recorded from subjects, when they are in normal and relax condition. EEG data have been recorded from 19 channels and are sampled with 256 Hz based on system of electrode placement. Hypnosis is normally preceded by a "hypnotic induction" technique. Traditionally this was interpreted as a method of putting the subject into a "hypnotic trance"; however, subsequent theorists have viewed it differently, as a means of heightening client expectation, defining their role, focusing attention, etc. There are enormous varieties of different induction techniques used in hypnotism. In this research hypnosis, induction has been done by playing an audiotape based on Waterloo-Stanford criterion [11], so the method and time of hypnosis induction was the same for all of the subjects. The first 15 minutes of this tape are related to the hypnosis induction and the remaining 30 minutes are related to 12-item Waterloo-Stanford group scale (WSGS) of hypnotic susceptibility measuring. Based on the subject s reaction to these 12 items, a score of hypnotisability is determined for each subject. The WSGS scores are between 12 and 60. Based on these scores the subjects divided into three groups, low (WSGS scores are between 12 and 22), medium (WSGS scores are between 23 and 41) and high (WSGS scores are between 42 and 60). In our dataset, 4 subjects were low hypnotisable, 18 subjects were medium and 10 subjects were high hypnotisable. 68

3 Golnaz Baghdadi and Ali Motie Nasrabadi 2.2.Empirical Mode Decomposition EMD method is an adaptive data driven decomposition procedure that decomposes a time series into a finite and often small number of intrinsic mode functions (IMFs), each of which must satisfy the following definition: (1) Number of extrema = number of zero-crossings ± 1. (2) At any point, the mean value of the upper and lower envelope is zero. The IMFs, xi(t), of a signal y(t), is found as follows: (1) Compute the mean of upper and lower envelopes of signal, m(t) (2) Subtract to the signal to obtain zi(t)=y(t)-m(t). (3) Check if zi(t) is an IMF, then zi(t) is the first IMF of y(t). If it is not an IMF, zi(t) is treated as the original signal and (1) (3) are repeated (4) Separating zi(t) from y(t), we get yi(t)=y(t)-zi(t). yi(t) is treated as the original data, and by repeating the above processes, the second IMF of y(t) could be obtained [9]. The second step is applying Hilbert transform to each IMF, in order to compute the instantaneous frequency and amplitude at each time. X(t) in the following equation is the Hilbert transform of Y(t): (1) Using equation (1), instantaneous frequency, If(t), and instantaneous amplitude, a(t), are defined as[9],[12]: (2) The next step after estimating instantaneous frequency and amplitude is separating each of the considered frequency bands along time. That is done by computing the sum of the IMFs, which belong, at each sample, to the considered frequency band [13]. The reason for using this method to extract the different frequency bands instead of merely filtering each band is that band pass filtering has a number of drawbacks that have been briefly introduced in refs. [9], [14]. 69

4 2.3.Detrended Fluctuation Analysis and Scaling Exponent The scaling exponent is important in the characterisation of long-range temporal correlations in finite-length sequences [2]. Temporal coherence describes the correlation or predictable relationship between signals observed at different moments in time [15]. Self-similarity parameter, or scaling exponent, which is the result of detrended fluctuation analysis (DFA) is calculated as follows [16], [17]: (1) Alter the given signal xi into integrated series, ix represents the average value of xi: (2) Calculate the root-mean-square deviation from the local trend (fluctuation), Xn, over every window with size L: (3) (3) This detrending is repeated over the whole signal at a range of different window sizes L, and a graph of log (L) against log (F(L)) is constructed. (4) The scaling exponent is calculated as the slope of a straight line fit to this log-log graph using least squares. By this method, the scaling exponent can have different values[16]: (4) 3. Data Analysis and Results 3.1.Effect of Hypnosis Induction on Scaling Exponent Comparisons between the mean scaling exponent of the hypnosis and normal EEG have been carried out using paired sample t test [18]. Figure1 represents a plot of the natural logarithm of F(L) as a function of the natural logarithm of L in order to show the dependence of F(L) on the window size L and to show the scaling properties of the analysed normal and hypnosis EEG signals. 70

5 Golnaz Baghdadi and Ali Motie Nasrabadi Figure 1. The natural logarithm of F(L) as a function of the natural logarithm of L, in delta band of channel 1 of a low hypnotisable subject; (a) hypnosis EEG, (b) normal EEG The comparison between hypnosis and normal EEG has been performed in all channels and in all frequency bands. The range of the p-values in these tests in all 19 channels has been shown in table1 for different frequency bands. Table 1. The results of The Paired Sample T-TEST Frequency Bands Range of P-values in 19 brain channels Delta <<0.01 Theta <<0.01 Alpha <<0.01 Beta <0.01 Gamma <

6 According to these p-values, the null hypotheses are rejected. That means the hypnosis induction has significant effect on the scaling exponent values of the EEG signal. This effect is different in various frequency bands, see table 2 and figure 2. Table 2. Effect of Hypnosis Induction on Scaling Exponent of EEG Signal The Range of The Range of Effect of Scaling Scaling Hypnosis Exponents in Exponents in Induction on Normal EEG Hypnosis Scaling EEG Exponent Values Delta [ ] [ ] INCREASE (+0.1±0.09) Theta [ ] [ ] INCREASE (+0.15±0.07) Alpha [ ] [ ] INCREASE (+0.1±0.05) Beta [ ] [ ] INCREASE (+0.03±0.03) Gamma [ ] [ ] DECREASE (-0.09±0.085) Figure 2. Scaling exponent values in normal and hypnosis EEG in different frequency bands. 72

7 Golnaz Baghdadi and Ali Motie Nasrabadi In accord with the recorded result in table 2, hypnosis induction increases in the scaling exponent values in lower frequency band but in higher frequency band, the scaling exponent values decrease after hypnosis induction. 3.2.Effect of Hypnotisability Induction on Scaling Exponent Performing one-way analysis of variance (ANOVA) [19] on the scaling exponents of the hypnosis EEG (the average of scaling exponents along hypnosis EEG) in different brain channels (19 channels) and in various frequency bands, shows that there is no significant difference between three hypnotisable groups in all of the channels and in all frequency bands. In addition, no linear combination of all channels was found that have relation with hypnotisability. Then the relation of the following ratio with hypnotisability was investigated. All obtained P-values of the statistical analysis of the mentioned ratio in all channels and also in all frequency bands were more than 0.05 and the best result of these investigations belongs to the ratio in delta band, in channel C4 which can make difference between three hypnotisable groups by 90% of confidence interval (p- value=0.1). So it is claimed that no significant relation is found between the scaling exponents of the DFA algorithm and hypnotisability in different frequency bands of hypnosis EEG. 4. Discussion and Conclusion In this study, all the scaling exponents for the normal and hypnosis EEG were investigated in different brain channels and in various frequency bands. Lee et al. (2007), in their study on the scaling exponent of the EEG signal on hypnosis and waking condition, reported that all the scaling exponent were greater than 0.5 and less than 1.5, regardless of the condition. However, in the current study, investigating the EEG signal in different frequency bands shows that the scaling exponent in high frequency band (Gamma) is less than 0.5 in both hypnosis and normal EEG. According to table 2 and the discussion about possible values of the scaling exponent that was done in part II.C, it can be claimed that from low frequency band (Delta) toward high frequency band (Gamma), temporal correlation of EEG signal starts to decrease which in gamma band the EEG signal changes to a uncorrelated signal in hypnosis and also normal EEG. It means that in high frequency band, the signal is more unpredictable than in low frequency band. In this study, it was also shown that hypnosis induction has significant effect on scaling exponent of EEG signal. Lee et al. reported that the fractal dynamics of EEG rhythm are more random and less correlated in the hypnotic condition than in the waking condition [2]. However, in current study it was found that this effect is different in various frequency bands. In delta, theta and alpha bands, the temporal correlation of the hypnosis EEG is more than normal EEG. The detection of temporal correlation attributes to the presence of memory in a physiological sense [20]. In beta band increasing the temporal correlation after hypnosis suggestion is less than the previous bands and the tendency of the scaling exponents values are toward uncorrelated signals (scaling exponent?0.5). 73

8 Finally, in gamma frequency band the scaling exponents values in both hypnosis and normal EEG relate to an anti correlated signal (scaling exponent<0.5) and this anti correlation increases after hypnosis suggestion. Disappearing temporal correlation after hypnosis induction in high frequency band shows that the changes of the EEG signal's dynamic is more than low frequency band in hypnosis. In fact, this unpredictable change reduces the temporal correlation along the signal. More high frequency activation of the EEG signal in hypnosis proves that hypnosis is not a sleep. The relation between hypnotisability and scaling exponent values was also investigated in this paper. Lee et al. in their study found no significant correlation between HIP- induction score and scaling exponent but they found that the eye-roll sign was significantly negatively correlated with the scaling exponents at channels F3, C4, O1 and O2. The results of statistical analysis of the current study show that there is no significant relation between hypnotisability (WSGS-induction score) and temporal correlation of signal in both hypnosis and normal condition, in various frequency bands. Finally, this study suggests that hypnosis induction can change the temporal correlations of EEG signal (the properties of the neuronal networks) and that these changes are various in different EEG frequency bands. Reference: [1] P. Gifani, et al, Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification, Journal of the Franklin Institute, vol. 344, 2007, pp [2] J. S. Lee, et al. Fractal Analysis Of EEG In Hypnosis And Its Relationship With Hypnotisability, Intl. Journal of Clinical and Experimental Hypnosis, vol.55, no.1, 2007, pp [3] V. V. Nikulin, T. Brismar, Long-range temporal correlations in alpha and beta oscillations: Effect of arousal level and test-retest reliability, Clinical Neurophysiology, vol. 115, 2004, pp [4] L.M. Parish, et al. Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus. Neuroscience, vol.125, 2004, pp [5] P. A. Watters, F. Martin, A method for estimating long-range power law correlations from the electroencephalogram, Biological Psychiatry, vol. 66, 2004, pp [6] K. Linkenkaer-Hansen,V. V. Nikouline, J. M. Palva, R. J. Ilmoniemi, Longrange temporal correlations and scaling behavior in human brainoscillations, Jr of Neuroscience, vol. 21, 2001, pp [7] V.V. Nikulin, T. Brismar, Long-range temporal correlations in electroencephalographic oscillations: relation to topography, frequency band, age and gender, Neuroscience, vol.130, 2005, pp [8] C.K. Peng, S. Havlin, H.E. Stanley, A.L. Goldberger,Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series, Chaos, vol.5, no.1, 1995, p. 827 [9] N. E. Huang, et all, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. Royal Soc. London A, vol. 454, 1998, pp [10] D. Han, W. Li, X. Lu, Y. Wang, M. Li, Representation Interest Point Using Empirical Mode Decomposition and Independent Components Analysis, ISMIS 2006, LNAI 4203, pp , [11] K. S. Bowers, Waterloo-Stanford Group Scale of Hypnotic Susceptibility, Form C: Manual and Response Booklet, International Journal of Clinical Hypnosis, vol. 46, No.3, 1998, pp [12] L. Sun 1, M. Shen 1, F. H. Y. Chan 2, P. J. Beadle, Instantaneous Frequency Estimate of Nonstationary Phonocardiograph Signals Using Hilbert Spectrum, Proceedings of the 2005 IEEE Engineering in Medicine and Biology, pp , 2005 [13] H. Sharabaty, J. Martin, B. Jammes, D. Esteve, Alpha and Theta Wave Localisation Using Hilbert- Huang Transform: Empirical Study of the Accuracy, Information and Communication Technologies, ICTTA '06. 2nd, pp [14] C. M. Sweeney-Reed, S. J. Nasuto, A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition, Journal of Computational Neuroscience, Vol. 23, no.1, pp , [15] M. E. Anderson, G. E. Trahey A seminar on k-space applied to medical ultrasound, Department of Biomedical Engineering Duke University, 2006, [16] C. K. Peng, SV. Buldyrev, S. Havlin, M. Simons, HE. Stanley, AL. Goldberger, Mosaic organisation of DNA nucleotides. Phys Rev E, vol. 49, 1994, pp [17] D. Aba?solo, R. Hornero, J. Escudero, and P. Espino, A study on the possible usefulness of detrended fluctuation analysis of the electroencephalogram background activity in Alzheimer s disease, IEEE Trans. Biomed. Eng., vol. 55, pp , 2008 [18] D. Arkkelin, Using SPSS to Understand Research and Data Analysis, [19] R. V. Hogg, J. Ledolter, Engineering Statistics. MacMillan Publishing Company, 1987 [20] J. Bhattacharya, J. Edwards, A. N. Mamelak, E. M. Schuman, Long-Range Temporal Correlations In The Spontaneous Spiking Of Neurons In The Hippocampal Amygdala Complex Of Humans, Neuroscience, vol.131, 2005, pp

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