THE THREE DIMENSIONAL ELECTROENCEPHALOGRAM MODEL FOR BRAINWAVE SUB BAND OF POWER SPECTRAL

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1 THE THREE DIMENSIONAL ELECTROENCEPHALOGRAM MODEL FOR BRAINWAVE SUB BAND OF POWER SPECTRAL N. Fuad 1,2 and M.N.Taib 2 1 Faculty of Electrical & Electronics Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor. 2 Faculty of Electrical Engineering, University Teknologi MARA, Shah Alam, Selangor, Malaysia, norfaiza@uthm.edu.my Abstract: Human Brain produces four bands of frequency such as delta, alpha, beta and theta. Every frequency bands have different range delta (0.2-3 Hz), theta (3-8 Hz), alpha (8-12 Hz) and beta (12-30 Hz) can be used for analyzing brain activities. The power spectral density (PSD) is generated from frequency band through electroencephalogram (EEG) application. In this paper, the proposed methods for producing three dimension (3D) EEG model using signal and image processing techniques are presented. The EEG raw signal has been recorded from healthy subjects. Development of 3D EEG models involves preprocessing of EEG raw signals and construction of spectrogram images. The artifact removal and band pass filter are implemented for preprocessing signal stage. The resultant image which is two-dimensional (2D) EEG image or spectrogram is constructed via Short Time Fourier Transform (STFT). Optimization, color conversion, gradient and mesh algorithms are image processing techniques which have been implemented to produce this model. The relationships between time, amplitude value and power value as three parameters for 3D EEG model are clearly presented. These have become significant to the proposed methods. Keywords- Brainwave, Electroencephalographic (EEG), EEG raw signal, 2D EEG image, 3D EEG model 1. INTRODUCTION A normal human brain contains a hundred billions of neurons as figured out by the scientists. About 250,000 neurons are connected to a single neuron. The information is processed and sent by a normal brain to the whole human body. An electrical power is generated and this signal is called wave [1-4]. Brain comprises a pair of hemisphere namely the left hemisphere and the right hemisphere. The language, arithmetic, analysis and speech are performed in the left side of the brain. The right side of hemisphere is dominant in the cognitive tasks such as understanding, emotion, perceiving, remembering and thinking [5-8]. The brain hemispheres are not exactly symmetrical, but the degree of asymmetry between the two hemispheres is insignificant [7]. Brain asymmetry means that both brain hemispheres have to work closely to ensure a smooth operating and having an overly dominant hemisphere is invariably not a good thing in human brainwave. When a person 8

2 is relaxed the functions of both the brain hemispheres are symmetrical [7]. Asymmetry technique can identify which brain hemisphere is activated at a certain time and condition. The happiness and good health are affected by healthy lifestyles [9]. According to a psychiatrist, Dr. Paul Sorgi, the feeling of stress and mental illness are caused by disability of mind balance control and imbalance lifestyles which are affected by physical and psychology [11]. In contrast, the happiness, satisfaction, healthy and free to communicate with each other is achieved by managing the mind balance [10-12]. Many studies proved that longer and healthier life can be obtained to ensure the human being live in balance in order to improve human potential. Recently, the interests to discover the methods for balancing of the brain have increased [13-15] by using auditory and visual methods in brainwave entrainment that results in more waves that are similar to the frequency following response [14-16]. There is another method to perform the test namely Transcranial Magnetic or Electric Stimulation. This traditional method include massages, meditation and acupunctures [13-15].From the previous researches and the review of literatures, most human want to feel happy and healthy. Hence, a balance life is become from balance thinking or mind from the brain [1, 17]. Nowadays, there is no a scientific proves of brainwave balancing index using EEG. But there are some techniques or devices to help human to fell clam and balanced. The electroencephalogram (EEG) is developed to act as a device to collect brainwave signal and the frequency of θ-theta, δ-delta, α-alpha and β-beta bands [19]. The EEG raw data is produced in spectral pattern. The power for each spectral power has the frequency bands: 4-8 Hz is θ-theta band, Hz (δdelta band), 8-13 Hz (α-alpha band) and Hz (βbeta band) [20]. These components are utilized and hypothesized to produce the variation of neuronal assemblies [1, 21]. Referring to the theory, beta band is the lowest amplitude but the highest frequency band while delta band is opposite to beta band. High beta is occurred when human is inactive, not busy or anxious thinking but the low beta is occurred in positive situations. Human activities such as closing the eyes, relax/reflecting mode and all activities with inhibition control are affected by alpha band. The theta band is occurred when human in stress mode and light sleep also it has been found in baby activities. When human is in profound sleep mode, the delta band is produced [3]. However, EEG topography is produced by several software or toolboxes such as EEGLAB in Matlab embedded module [22]. LORETA is an electromagnet tomography in low resolution used for Alzheimer patients to produce EEG spectrogram or topography [23]. MEG and EEG signal are normally displayed by using the brainstorm approach [24]. Normally, EEG signals are represented by time domain and the plot of domain is displayed in timeamplitude. In the same time, some additional information can be found from frequency domain signal [3]. The artifact in EEG can be re-referenced in average of EEG power density spectrum analysis. The result is analyzed using an algorithm of Fourier Transform (FT) algorithm [25]. Discrete Fourier Transform (FFT) is used to estimate the smoothed periodograms by the power spectral density [26]. There are several methods to analize EEG signal in time-frequency domain and Short Time Fourier Transform (STFT) is a method to convert an EEG signal become a two dimension (2D) image [27]. However, some differences are recognized among 3D and 2D in term of implementation in technology field. For examples, parameters for 2D baby scanning are height and width and 3D baby scanning are height, width and depth [28]. There are another research done in 3D implementation such as crystal surfaces [29], interfacing computer to the brain [30] and three dimension acoustic assessment [31]. Therefore, some methods of experiment to produce 3D EEG signal has been proposed in this paper. However, the focusing result is only a subject due to right frontal (RF) brainwave. 9

3 2. METHODOLOGY Figure 1 shows the flow diagram of methodology. Initially, EEG signals were collected from 51 volunteers. Then, the EEG signals were preprocessed 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. 2.1 EEG signal recording This research involved volunteers of samples which are students and lecturers. 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 the ethics committee from UiTM. Figure 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-checker equipment was used to maintain the impedance to below than 5kΩ. The MATLAB and SIMULINK are used to process the data with the intelligent signal processing technique. EEG signal recording (EEG raw data) Preprocessing (Artifact removal and Filtering delta, theta, alpha and beta) Development of 2D images for each band Development 3D EEG models for each band Figure 1. Flow diagram of methodology The volunteers are required to answer the fifteen questions in Brain Dominance Questionnaires prior to EEG recording [32]. Then, the score is calculated after the questionnaires are completed. The score will determine the group or balanced brain index of each sample. This balanced brain index is produced from the previous experiment [33]. Table 1 shows the balanced brain index with the descriptions. Figure 2. Experimental setup 10

4 Table 1: The index level for balanced brain Index level Index level 1 Index level 2 Index level 3 Index level 4 Index level 5 Description Unbalanced brain Less balanced brain Moderately balanced brain Balanced brain Highly balanced brain 2.2 Signal preprocessing The EEG raw data was processed separately after data collection. The filter of band pass and artifact removal was included in EEG signal preprocessing. The artifacts may be produced when the eyes of volunteers blink. The artifacts can be removed by setting a threshold value in MATLAB tools. The setting of threshold values were below than -100μV and greater than 100μV. Only the meaningful and informatics signal were occurred within -100μV to 100μV. The Hamming windows were used to design the band pass filter with the rate of overlapping of 50% for the frequency; 4-8 Hz is θ- theta band, Hz (delta-δ band), 8-13 Hz (alpha-α band) and Hz (beta-β band) D EEG Images The STFT was used to produce the2d EEG image or spectrogram in 436x342 pixels of image size for Fp1 and Fp2 channel. Each band of frequency was set in a spectrogram image. The θ- theta band was set from 4Hz to 8 Hz, Hz (δdelta band), 8-13 Hz (α-alpha band) and Hz (βbeta band). This method was used for motor imagery EEG signal classification [22,23] and epileptic seizures detection using EEG signal. [24,25]. Equation 1 was implemented to analysis the signal in time frequency domain. The EEG signal, x(t), the window function, w(t) and signiture of complex conjugate, * are stated in STFT. The signal changed in time and performed using STFT. The small window of data in one time was used to map the signal to 2D function of time and frequency. STFT is yield by multiplication between Fourier Transform (FT) with window function. STFT ( w) x [ j2 ft (1) ( t, f ) x( t).( t t'). dt] 2.4 3D EEG Models 3D EEG models have been developed from EEG spectrogram using image processing techniques. Some techniques or algorithms such as gradient, color conversion, optimization and mesh algorithms were integrated to developed this model, while the spectogram images are represented in RedGreenBlue (RGB) color. Color conversion was implemented to transform spectogram of RGB to spectogram of gray scale. Gray scale images were used in a data matrix (I) which the values represent intensity within some range which are 0 (black) and 255 (white). Gray scale is the most commonly used images within the context of image processing. Then, Optimization Options Reference (OOR) was implemented to gray scale pixels image for optimization technique. There were severals options in OOR using MATLAB software but for this research, DiffMaxChange (Maximum change in variables for finite differencing) option have been chosen. The natural shape can be found from pixels value. This shape related to the maximum of certain energy function computed from the surface position and squared norm. A finite number of points were generated for the height of the optimized surface. Then the matrices of pixels value were resized using Gradient and Mesh algorithm into vectors. Two vector arguments replaced the first two matrix arguments, length(x) = n and length(y) = m where [m, n] = size (z). A vectors x is included matrix X (rows) and a vectors y is for matrix Y (columns). Matrix X and Y can be evaluated as features using array module in MATLAB s software. 3 RESULTS AND DISCUSSION This section shows the result from a subject for RF brainwave (Fp2 probe). The development of 3D EEG models has been successful using optimization; gradient and mesh algorithms. e 11

5 3.1. Preprocessing for 1D EEG signal The results for preprocessing process have been shown in this section. A raw EEG signal for a sample is from Fp2 with 180μV as maximum value shown in Figure 3(a). Figure 3(b) shows filtered EEG signal after artifact removal. This technique used to eliminate artifact from raw EEG signal during recording. The artifacts are noises in raw EEG signal and it happen when volunteer s blink his or her eyes. This filtered EEG signal is in time domain plot with maximum value 100 μv peak to peak of signal. exhibits the higher magnitude of signal and vice versa. However, figure 5 (appendix B) shows the PSD signal for each frequency. All these signals are generated from the EEG signal that has been filtered and implemented use Fourier Transform (FT). The PSD value for δ-delta band is the highest while β- beta band became the lowest value. These results were reflected on the shape produced of the 3D EEG model for every sub bands in figure 7 (appendix D) D EEG Images This section shows the 2D EEG images or spectrogram as a result after implemented STFT algorithm. This algorithm used to analyze and produce color scheme for spectrogram from 1D EEG signal in figure 3(b). 2D EEG images or spectrogram is in time frequency domain as shown in figure 6(a-d) or appendix C. These spectrograms generated from right frontal brainwave for four frequencies; δ-delta band, θ-theta band, α-alpha band and β-beta band. (a) (b) Figure 3: (a) A sample of EEG raw data in time domain (b) A sample of filtered EEG data in time domain. Figure 4(a-d) in appendix A shows filtered EEG signal using band pass filter with 256 Hz frequency sampling. The band pass filter used to generate frequency signal from right frontal brainwave for four frequencies; δ-delta band, θ-theta band, α-alpha band and β-beta band. The lower frequency band 3.3 3D EEG Models The 3D EEG model is generated from 2D EEG image or spectrogram. The RGB spectrogram converted to gray scale image or spectrogram. This image is also known as an intensity image and it represents color on image in some range pixel value. The range is from 0 (black) to 255 (white). Then the optimization value from pixels generated using Optimization Option Reference (OOR) in Matlab software. The 3D EEG model is shown in figure 7(ab) or appendix D. These results have been implemented Mesh and Gradient algorithm after OOR. These 3D EEG model generated from right frontal brainwave for four frequencies; δ-delta band, θ-theta band, α-alpha band and β-beta band CONCLUSION The observation in figure 4(a-d) shows there are two parameters in 1D EEG signal. The amplitude 12

6 value is a dependent parameter corresponded to the time as an independent parameter. However, 2D EEG image or spectrogram correlated two parameters as shown in figure 6(a-d) or appendix C. The frequency value is a dependent parameter and time as an independent parameter. Therefore there is a relationship between two parameters can be observed in 1D EEG signal and 2D EEG image or spectrogram. Figure 7(a-d) or appendix D shows 3D EEG model. From this model, the relationships between three parameters (time, frequency and magnitude power) are clearly displayed. Frequency value and time are dependent parameters and power as an independent parameter. Besides that, the spectral of power for each time and frequency value displayed clearly in 3D EEG model compared to 2D EEG image. The maximum PSD value also clearly presented. Therefore, 3D EEG model will provided more information. These characteristics become as the advantages of 3D EEG model and can be highlight as the significant of the propose technique. ACKNOWLEDGMENT 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. REFERENCES [1] Y.M. Randall and C. O Reilly, Computational Exploration in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press London, [2] D. Cohen, The Secret Language of the Mind, Duncan Baird Publishers, London, [3] M. Teplan, "Fundamentals of EEG Measurement.", Measurement Science Review, vol. 2, pp. 1-11, [4] E. R. Kandel, J. H. Schwartz, T. M. Jessell, Principles of Neural Science, Fourth Edition, McGraw-Hill, [5] E. Hoffmann, "Brain Training Against Stress: Theory, Methods and Results from an Outcome Study", version 4.2, October [6] R. W. Sperry, "Left -Brain, Right Brain," in Saturday Review:speech upon receiving the twenty-ninth annual Passano Foundation Award, pp , [7] J. H. Suresh Kanna, "Quantitative EEG Parameters for Monitoring and Biofeedback During Rehabilitation After Stroke," International Conference on Advanced Intelligent Mechatronics, pp , [8] Zunairah Haji Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani S.Abdul Kadir, Norizam Sulaiman, and Mahfuzah Mustafa. Establishing the fundamental of brainwave balancing index (BBI) using EEG, presented at the 2 nd Int. Conf. on Computional Intelligence, Communication Systems and Networks (CICSyN2010), Liverpool, United Kingdom, [9] P. J. Sorgi, The 7 Systems of Balance: A Natural Prescription. [10] R. W. Sperry, "Some Effects of Disconnecting the Cerebral Hemispheres," in Division of Biology California Institute of Technology, Pasadena. California, 1981, pp [11] P. J. Sorgi, The 7 Systems of Balance: A Natural Prescription for Healthy Living in a Hectic World Health Communications Incorporated, [12] E. R. Braverman, The Edge Effect: Archive Total Health and Longevity: Sterling Publishing Company, Inc.,2004. [13] Z. Liu, L. Ding, "Integration of EEG/MEG with MRI and fmri in Functional Neuroimaging," IEEE Eng Med Biological Magazine, vol. 25, pp , [14] U. Will and E. Berg, "Brain Wave Synchronization and Entrainment to Periodic Acoustic Stimuli," Neuroscience Letters, vol. 424, pp , [15] B.-S. Shim, S.-W. Lee, "Implementation of a 3 Dimensional Game for Developing Balanced Brainwave," presented at 5 th International Conference on Software Engineering Research, Management & Applications, [16] Rosihan M. Ali and Liew Kee Kor, Association Between Brain Hemisphericity, Learning Styles and Confidence in Using Graphics Calculator for Mathematics, Eurasia Journal of Mathematics, Science and Technology Education,vol. 3(2), pp , [17] M. Hutchison, Mega Brain Power: Transform Your Life with MindMachines and Brain Nutrients: Hyperion, [18] Zunairah Hj. Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani S. Abdul Kadir, Norizam Sulaiman and Zodie Mohd Hanafiah, Development of Brainwave Balancing Index Using EEG, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, pp ,

7 [19] Jansen BH, Cheng W-K. Structural EEG analysis: an explorative study., Int J Biomed Comput 1988; 23: [20] L. Sornmo, and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications. Burlington, MA: Elsevier Academic Press, [21] N. Hosaka, J. Tanaka, A. Koyama, K. Magatani, The EEG measurement technique under exercising, Proceedings of the 28 th IEEE EMBS Annual International Conference, New York City, USA, Sept 2006, pp [22] A. Delorme, and S. Makeig, The EEGLAB, Internet sccn.ucsd. edu/eeglab, vol. 2, no. 004, pp [23] C. Babiloni, G. Binetti, E. Cassetta, D. Cerboneschi, G. D. Forno, C. D.Percio, F. Ferreri, R. Ferri, B. Lanuzza, C. Miniussi, D. V. Moretti, F. Nobili, R. D. Pascual-Marqui, G. Rodriguez, G. L. Romani, S. Salinari, F. Tecchio, P. Vitali,O. Zanetti, F. Zappasodi, P. M. Rossin., Mapping distributed sources of cortical rhythms in mild Alzheirmer's disease. A multicentric EEGstudy, NeuroImage, vol. 22, pp , [24] K. N. Diaye, R. Ragot, L. Garnero, V. Pouthas, What is common to brain activity evoked by the perception of visual and auditory filled durations? A study with MEG and EEG co-recordings, Cognitive Brain Research,vol. 21, pp. pp , [25] C. Babiloni, R. Ferri, G. Binetti, F. Vecchio, G. B. Frisoni, B. Lanuzza, C. Miniussi, F. Nobili, G. Rodriguez, F. Rundo, A. Cassarino, F. Infarinato, E. Cassetta, S. Salinari, F. Eusebi, and P. M. Rossini, "Directionality of EEG synchronization in Alzheimer's disease subjects," Neurobiology of Aging, vol. 30, pp , [26] A. Piryatinska, G. Terdik, W. A. Woyczynski, K. A. Loparo, M. S. Scher, and A. Zlotnik, "Automated detection of neonate EEG sleep stages," Computer Methods and Programs in Biomedicine, vol. In Press, Corrected Proof. [27] M. T. Pourazad, Z. K. Mousavi, and G. Thomas, "Heart sound cancellation from lung sound recordings using adaptive threshold and 2D interpolation in time-frequency domain," in Proceedings of the 25th Annual International Conference of the IEEE, 2003, pp [28] Ohbuchi.R, Incremental 3D ultrasound imaging from a 2D scanner, Conference in Biomedical Computing, Atlanta, [29] A. I. Kochaev R. A. Brazhe, Mathematical modeling of elastic wave propagation in crystals: 3Dwave surfaces, Department of Physics, Ulyanovsk State Technical University, Rusia, 2011 [30] Dongmei Hao, Hongwei Zhang, and Naigong Yu High Resolution Time-Frequency Analysis for Event-Related Electroencephalogram, Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21-23, Dalian, China, 2006 [31] A.J.B. Tadeu*, L. Godinho, P. Santos, Performance of the BEM solution in 3D acoustic wave scattering, University of Coimbra, Portugal,Advances in Engineering Software vol.32 pp , 2001 [32] L. Mariani. (1996, 1 May 2010). Brain-dominance questionaire.available: questionnaires/ lrquest/ lrquest.htm [33] Z. H. Murat, M. N. Taib, S. Lias, R. S. S. A. Kadir, N. Sulaiman, and M. Mustafa, "The Conformity Between Brainwave Balancing Index (BBI) Using EEG and Psychoanalysis Test," International Journal of Simulation Systems, Science & Technology, vol. 11, pp ,

8 Appendix A (a) (b) (c) (d) Figure 4. 1D EEG signal (Amplitude vs time) for (a) Delta-δ band (b) Theta-θ band (c) Alpha-α band (d) Beta-β band 15

9 Appendix B (a) (b) (c) Figure 5. Power Spectral Density (PSD) signal for (a) δ-delta band (b) θ-theta band (c) α-alpha band (d) β-beta band (d) 16

10 Appendix C (a) (b) (c) (d) Figure 6: 2D EEG image (Frequency vs time) for (a) δ-delta band (b) θ-theta band (c) α-alpha band (d) β-beta band 17

11 Appendix D (a) (b) (c) (d) Figure 7: 3D EEG model for (a) δ-delta band (b) θ-theta band (c) α-alpha band (d) β-beta band 18

THE THREE DIMENSIONAL ELECTROENCEPHALOGRAM MODEL FOR BRAINWAVE SUB BAND OF POWER SPECTRAL

THE THREE DIMENSIONAL ELECTROENCEPHALOGRAM MODEL FOR BRAINWAVE SUB BAND OF POWER SPECTRAL N. Fuad 1,2 and M.N.Taib 2 1 Faculty of Electrical & Electronics Engineering, Universiti Tun Hussein Onn Malaysia,