brain balancing pattern between left and right frontal using mean relative power ratio via three...

Procedia - Social and Behavioral Sciences 97 ( 2013 ) 38 – 45

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.Selection and/or peer-review under responsibility of the Universiti Malaysia Sarawak.doi: 10.1016/j.sbspro.2013.10.202

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The 9th International Conference on Cognitive Science

Brain balancing pattern between left and right frontal using mean relative power ratio via three dimensional EEG model

N.Fuada,b*, M.N.Taiba , R.M.Isaa, A.H.Jahidina, W.R.W.Omara

a Faculty of Electrical & Electronic Engineering,Universiti Tun Hussein Onn Malaysia, 43000 Batu Pahat, Johor, Malaysia.b Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Abstract

This paper discusses on power spectral density (PSD) characteristics extracted from three-dimensional (3D)electroencephalogram (EEG) model for different brain balancing indexes. EEG recording was conducted on 51 healthy subjects.Development of 3D EEG model involves pre-processing of raw EEG signals and construction of spectrogram images. Theresultant images which are two-dimensional (2D) were constructed via Short Time Fourier Transform (STFT). Optimization,color conversion, gradient and mesh algorithms have been implemented to produce 3D EEG model. Then, maximum PSD values were extracted as features from the model. These features were analyzed using mean relative power (MRP) ratio toobserve the pattern among different brain balancing indexes. The results show that by implementing MRP ratio technique, thepattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 3, index 4 and index 5using MRP ratio, MRP ratio gap pattern and difference mean relative (DMRP) ratio for left frontal (LF) and right frontal (RF).

© 2013 The Authors. Published by Elsevier Ltd.Selection and/or peer-review under responsibility of the Universiti Malaysia Sarawak

Keywords: power spectral density; 3D EEG model; brain balancing; mean relative power ratio

1. Introduction

EEG is the measurement of the brain's electrical activity in neurophysiologic aspects [1]. Spontaneous EEGoccurs when a person is assessed without taking into account the external stimulus. In general, the stimulus internalor external stimulus will produce EEG signal. This situation called an event-related potential (ERP) results. Normal human brain produces sine waves [3] with the strength of amplitude of 10- when a person is in the awakedstate [2]. Features such as a varying amplitude and frequency range become the main focus of researchers [4]. EEG can be identified with four bands; beta, theta, alpha and delta [5]. Recently, some studies on the state of the brain have been carried out involving the frequency band [6].

Brain is divided two parts known as left hemisphere and right hemisphere. Normally, the balanced brain betweenthe right hemisphere and left hemisphere is not significant because the two hemispheres of the brain are notsymmetric [7]. However, the brain is considered symmetric when there is a balance and a close relationship betweenthe two hemispheres to ensure all tasks are implemented properly. When there is one dominant hemisphere, thisaffects the unbalanced in human thought. When someone rationalizes both the functional hemisphere, it will be in

* Corresponding author. Tel.: +6012-7058158.E-mail address: [email protected]

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© 2013 The Authors. Published by Elsevier Ltd.Selection and/or peer-review under responsibility of the Universiti Malaysia Sarawak.

39 N. Fuad et al. / Procedia - Social and Behavioral Sciences 97 ( 2013 ) 38 – 45

symmetric. However some researchers identified which part of the hemisphere can be activated at a specific time and situation using asymmetric techniques [7].

From the previous researches and the review of literature, most of the human want to feel happy and healthy. While, a balance life is become from balance thinking or mind from the brain [8,9]. Nowadays, there is not found a scientific prove of brainwave balancing index using EEG. But there are some techniques or devices to help human felling calm and brain balancing.

Some researches analysis have done using sub-band power ratio such as in malignant brain tumours [10], anaesthesia [11], dementia of the Alzheimer's [12] and Schizophrenic patients [13]. EEG power ratio index mapping was used to monitor some patients with malignant brain tumours to generate and divide the low frequency (delta, theta) power by the high frequency (alpha, beta) power [10]. Arousal was defined as spontaneous movement, coughing or eye opening [11]. Five descriptors (median frequency, spectral edge frequency, total power, a frequency band power ratio, and the ratio of frontal to occipital power) were compared for their ability to predict imminent arousal using EEG. The researchers compared several types of quantitative EEG power measures (absolute and relative power, and ratios of power) to determine their regional distribution, and their association with changes in cognitive status and age pathologic changes in dementia of the Alzheimer's type [12]. Right/left ratio score comparable to the EEG alpha power ratio was computed for the measures of evoked potential power and for the peak-trough amplitude of individual components [13]. The power ratio analysis used for Schizophrenic patients, which were similar to visual EEG readings and useful in comparing individuals with amplitude differences [14].

Hence, this paper shows the results of 3D model for EEG. The features namely PSD is extracted from the model. Then the features extraction patterns of maximum PSD are analysed by mean relative power (MRP) ratio, MRP ratio gap pattern and difference of mean relative power (DMRP) ratio technique. These techniques are proven and it can implemented to recognise difference index of brain balancing.

2. Methodology

Figure1 shows the flow diagram of methodology. Initially, EEG signals were collected from volunteers. Then, the EEG signals were pre-processed to produce clean signals and filtering into four band frequencies delta, theta, alpha and beta. Next, the 2D image was produced from clean EEG signals and 3D EEG model have been developed from EEG spectrogram using image processing techniques.

The samples are among 51 volunteers which are 28 males and 23 females with an average age of 21.7. The data are collected from Biomedical Research and Development Laboratory for Human Potential, Faculty of Electrical Engineering, Universiti Teknologi MARA Malaysia (UiTM). All volunteers are healthy and not on any medication before the tests. These are performed and have fulfilled the requirement provided by ethics committee from (UiTM). Fig.2 shows the experimental setup for EEG signal recording. There were two channels and one reference to two earlobes used to collect or record EEG signal. These channels connected to gold disk bipolar electrode that complied with 10/20 International System. The sampling rate is 256Hz. Channel 1 positive was connected to the right hand side (RHS), Fp2. The left hand side (LHS), Fp1 was connected to channel 2 positive. FpZ is the point at the center of forehead declared as reference point. MOBIlab was used in wireless EEG equipment and the EEG signal was monitored for five minutes. The Z-MATLAB and SIMULINK are used to process the data with the intelligent signal processing technique. The pre-processing raw data, 2D EEG image and 3D EEG model developement are described in [15,16].


Fig.1. Flow diagram of methodology

Fig.2. Experimental setup

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3. Data Analysis

Three Dimension (3D) Signal produces spectral of power spectral density (PSD), then the maximum PSD is chosen as features to analize. Equation 1 and 2 are implemented to calculate the mean relative power (MRP) ratio for sub band left frontal (LF) and right frontal (RF) using Statistical Analysis Microsoft Excel 2010. (1) (2) where h is left frontal or right frontal, k - -8

- -3hz). N is the number of the sample.

The difference of mean relative power (DMRP) ratio sub band are done for LF and RF as equation 3 using Statistical Analysis Microsoft Excel 2010.

(3)

where k - - - -3hz). N is the number of the sample.

The brain balancing index was analyzed offline from previous work [17]. The percentage difference between left and right brainwaves was calculated from PSD values of EEG signals using the asymmetry formula as shown by (4). Table 1 shows the respective index and range of balance score. There were three groups; index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced).

%1002asymmetry ofPercentage xrightleftrightleftx (4)

Table 1. Brain balancing group with range of balance score

Index level Percentage difference between left and right Samples Index level 3 40.0%-59.9% 9 Index level 4 20.0%-39.9% 37 Index level 5 0.0%-19.9% 5

4. Result and Discussion

Three Dimension (3D) models were produced using optimization; gradient and mesh algorithms as depict in Fig.7 (a)-(h) (Appendix A). These show each of frequency bands for Fp1 and Fp2 channels. The 3D signal is spectral of PSD and a different maximum PSD produced by each frequency band. Eight 3D signals for channels Fp1 and Fp2 are produced by EEG sample. The 3D EEG model produced as depicted in Table 2.

NRatioRPkh

RatioMRPkh_

_

PowerEEGPowerkhRatioRPkh_

_

.___N

RatioMRPkRFN

RatioMRPkLFRatioDMRPk


Table 2. Data sample per index

Index level Samples 3D EEG model9 72

Index level 4 37 296Index level 5 5 40

4.1. Mean Relative Power (MRP) ratio

The observation from fig.3 (a-c) shows and agrees with the theory that lower frequency band exhibit highermagnitude and vice versa. Index 3 and index 5 showed right hemisphere is dominant except for beta band (equal value) and index 4 is dominant for all band. There is no obvious pattern that can see from these results.

(a) (b) (c)

Fig.3. MRP ratio plot for Left Frontal (LF) and Right Frontal (RF) (a) Index 3 (b) Index 4 (c) Index 5

4.2. Mean Relative Pattern (MRP) ratio gap pattern

Normally, samples are in calmness and not stress when their brain in balance. This could be due to the theory thatalpha is dominant during relaxing, close eyes but not sleep. From the observation in fig.4 (a-c) the gap for LF andRF alpha for index 3 (moderately balanced) is bigger compared to index 4 (balanced) and index 5 (highly balanced). The gap between LF and RF alpha become smaller and decrease. Because of small difference value between LF andRF alpha for index 5 (highly balanced), the curve is overlapped each other. Based on the results, there is evidencethat the gap between LF and RF alpha decrease when the number of index increase. The gap among LF and RF foralpha and beta band becomes smaller when the number of index increases.

4.3. DMRP ratio for Leftff Frontal (LF) and Right Frontal (RF)

Some patterns can be observed from fig.5 (a-c). From the theory of brain balancing, value of difference betweenLF and RF for sub band is small when brain is highly balanced and vice versa. Beta band shows the smallest difference between LF and RF for all indexes. The difference value between LF and RF for alpha band showsdecrement pattern and beta band increment pattern when the number of index is increase. The focusing is on alphaband and beta band pattern because the samples are in relax and not thinking when EEG recording.


(a) (b) (c)

Fig. 4. MRP ratio gap plot for Left Frontal (LF) and Right Frontal (RF) (a) Index 3 (b) Index 4 (c) Index 5

(a) (b) (c)

Fig.5. DMRP ratio plot for Left Frontal (LF) and Right Frontal (RF) (a) Index 3 (b) Index 4 (c) Index 5

Fig.6. Difference of MRP ratio plot for difference indexes

Fig.6 shows the DMRP ratio between LF and RF for alpha band and beta band when the number of index increases. The difference value for alpha band is decrement pattern when number of indexes increase. The highest value is 0.0107 (index 3) and lowest is 0.0039 (index 5). The result justified when human brain is in highly balance,the brainwave between LF and RF should be balanced and in smallest difference and vice versa. The differencevalue for beta band is increment pattern when number of indexes increase. The lowest value is 0.0001(index 3) and highest is 0.0041 (index 5). The samples were relaxing mode and open eyes (OE) when EEG data recording. Thebeta band was produced by eye stimulation when seeing the object surrounding.

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5. Conclusion

Experimental results revealed that maximum power spectral density from three dimension signals can be used as a characteristic to recognize the pattern of brain balancing groups. MRP ratio technique is done to analysis brain balancing for index 3 to index 5 and pattern recognition. There is no obvious pattern, that can be seen from the graphs but from the observation, right hemisphere is dominant except for beta band (equal value) in index 3 and index 5. Index 4 showed the right hemisphere is dominant for all bands. Then DMRP ratio technique was implemented. The gap pattern showed decrement pattern when the number of index is increase. Index 5 is highly balance compared to index 3 so the difference between LF and RF must smallest. After implement difference LF and RF for MRP ratio, alpha band showed decrement pattern (highest 0.0107, lowest 0.0039) and beta band is increment pattern (lowest 0.0001, highest 0.0041) when number of indexes increase.

Acknowledgments

N.Fuad would like to thank the members of Biomedical Research Laboratory for Human Potential, FKE, UiTM for their cooperation and kindness. Appreciation also goes to Advanced Signal Processing Research Group (ASPRG) for their support.

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Appendix A

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig.7. Three Dimension (3D) model for (a) Delta band from Fp1 channel (b) Delta band from Fp2 channel (c) Theta band from Fp1 channel (d) Theta band from Fp2 channel (e) Alpha band from Fp1 channel (f) Alpha band from Fp2 channel (g) Beta band from Fp1 channel (h) Beta band from Fp2 channel

brain balancing pattern between left and right frontal using mean relative power ratio via three...

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