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Mohd Nasir Taib - One of the best experts on this subject based on the ideXlab platform.
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Classification of EEG spectrogram image with ANN approach for Brainwave balancing application
International journal of simulation: systems science & technology, 2020Co-Authors: Mahfuzah Mustafa, Zunairah Haji Murat, Mohd Nasir Taib, Norizam Sulaiman, Siti Armiza Mohd ArisAbstract:In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in Brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of Brainwave balancing application with an optimized ANN model in training\ud by varying the neurons in the hidden layer, epoch, momentum rate and learning rate
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classification of intelligence quotient via Brainwave sub band power ratio features and artificial neural network
Computer Methods and Programs in Biomedicine, 2014Co-Authors: A. H. Jahidin, Mohd Nasir Taib, Md N Tahir, I M Yassin, Sahrim LiasAbstract:This paper elaborates on the novel intelligence assessment method using the Brainwave sub-band power ratio features. The study focuses only on the left hemisphere Brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the Brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
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assessment of acute ischemic stroke Brainwave using relative power ratio
International Colloquium on Signal Processing and Its Applications, 2013Co-Authors: W. R. W. Omar, Mohd Nasir Taib, Roshakimah Mohd Isa, R Jailani, Z SharifAbstract:This paper examines the Brainwave sub-band characteristics for three different group of stroke level based on the Relative Power Ratio (RPR) techniques. The EEG data sets have been collected from seventy four stroke patients with open eyes (OE) session. From these sessions, the sub-band RPR were calculated and further analysis is perform to determine the Brainwave characteristics due to group stroke level. The results show that by implementing RPR technique, the pattern of group stroke level, especially in the cognitive or thinking abilities can be clearly observed. It can be concluded that the value for beta ratio is higher for Advance Group (AG) compare to Intermediate Group (IG) and Early Group (EG). In contrast alpha ratio for EG have higher value compared to AG and IG stroke level. This indicates that the PSD ratios can discriminate the characteristics of Brainwaves for group stroke level assessment.
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Brainwave sub-band power ratio characteristics in intelligence assessment
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: A. H. Jahidin, Mohd Nasir Taib, N. Md Tahir, M. S. A. Megat Ali, Sahrim Lias, N. Fuad, W. R. W. OmarAbstract:This paper discusses on the Brainwave sub-band characteristics for different intelligence groups based on electroencephalogram (EEG) power ratio technique. The EEG datasets have been collected from 50 healthy subjects for two sessions; at relaxed, closed eye (CE) state as reference and at cognitively-stimulated state. In the stimulated state, subjects need to answer the intelligence quotient (IQ) test based on Raven's Standard Progressive Matrices (RPM). Sub-band power ratio from the two sessions were calculated and further analyzed to observe the pattern among different IQ groups. The results show that by implementing power ratio technique, the pattern of IQ groups, especially in the relaxed state can be clearly observed. It can be concluded that the value for alpha ratio is higher for high IQ group compared to low IQ group. In contrast to beta and theta ratio where high IQ groups have lower value compared to the low IQ group. This indicates that the ESD ratios can discriminate the characteristic of Brainwaves for intelligence assessment.
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development of Brainwave balancing index using eeg
Computational Intelligence Communication Systems and Networks, 2011Co-Authors: Zunairah Hj Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani Abdul S Kadir, Norizam Sulaiman, Zodie Mohamed HanafiahAbstract:In this research, Wireless EEG equipment via Bluetooth technology named g-Mobilab was used to measure the Brainwave signals in the right and left frontal area of the brain. The recorded EEG signals were channelled into an automatic artifact removal analysis whereby signals above values of 100 micro-volts were removed by means of a program using Matlab. Consequently, Power Spectral Density techniques and specific algorithm were employed to further enhance the EEG signals. The correlation between the left and the right Brainwaves were achieved using paired T test from SPSS. The results, which are Brainwave balancing index (BBI) and Brainwave dominance, were presented via Graphic User Interface (GUI). The outcome shows that BBI system could be established using EEG signals. These findings (Brainwave dominance and BBI) could be used as a straightforward indicator of one's ability to think and work leading to vast opportunity for constructive human potential advancement.
Sahrim Lias - One of the best experts on this subject based on the ideXlab platform.
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classification of intelligence quotient via Brainwave sub band power ratio features and artificial neural network
Computer Methods and Programs in Biomedicine, 2014Co-Authors: A. H. Jahidin, Mohd Nasir Taib, Md N Tahir, I M Yassin, Sahrim LiasAbstract:This paper elaborates on the novel intelligence assessment method using the Brainwave sub-band power ratio features. The study focuses only on the left hemisphere Brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the Brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
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Hemispheric Brainwave activity of violinists performing with music notation and without music notation
2013Co-Authors: Valerie Ross, Zunairah Haji Murat, Norlida Buniyamin, Zaini Mohd-zain, Sahrim LiasAbstract:Music has been known to improve learning and cognition. The ways in which musicians think and perform have increasingly become subjects of interest to scientists particularly in light of advances in neuroscience research. This study examines the Brainwave activity of a group of violinists as they perform. Using electroencephalography (EEG), the left and right Brainwaves of the musicians were recorded when they played a piece of music by first reading the score and then without reading the score. Results indicate that playing with music notation enhances left brain activity while playing without music notation enhances right brain activity.
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Brainwave sub-band power ratio characteristics in intelligence assessment
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: A. H. Jahidin, Mohd Nasir Taib, N. Md Tahir, M. S. A. Megat Ali, Sahrim Lias, N. Fuad, W. R. W. OmarAbstract:This paper discusses on the Brainwave sub-band characteristics for different intelligence groups based on electroencephalogram (EEG) power ratio technique. The EEG datasets have been collected from 50 healthy subjects for two sessions; at relaxed, closed eye (CE) state as reference and at cognitively-stimulated state. In the stimulated state, subjects need to answer the intelligence quotient (IQ) test based on Raven's Standard Progressive Matrices (RPM). Sub-band power ratio from the two sessions were calculated and further analyzed to observe the pattern among different IQ groups. The results show that by implementing power ratio technique, the pattern of IQ groups, especially in the relaxed state can be clearly observed. It can be concluded that the value for alpha ratio is higher for high IQ group compared to low IQ group. In contrast to beta and theta ratio where high IQ groups have lower value compared to the low IQ group. This indicates that the ESD ratios can discriminate the characteristic of Brainwaves for intelligence assessment.
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development of Brainwave balancing index using eeg
Computational Intelligence Communication Systems and Networks, 2011Co-Authors: Zunairah Hj Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani Abdul S Kadir, Norizam Sulaiman, Zodie Mohamed HanafiahAbstract:In this research, Wireless EEG equipment via Bluetooth technology named g-Mobilab was used to measure the Brainwave signals in the right and left frontal area of the brain. The recorded EEG signals were channelled into an automatic artifact removal analysis whereby signals above values of 100 micro-volts were removed by means of a program using Matlab. Consequently, Power Spectral Density techniques and specific algorithm were employed to further enhance the EEG signals. The correlation between the left and the right Brainwaves were achieved using paired T test from SPSS. The results, which are Brainwave balancing index (BBI) and Brainwave dominance, were presented via Graphic User Interface (GUI). The outcome shows that BBI system could be established using EEG signals. These findings (Brainwave dominance and BBI) could be used as a straightforward indicator of one's ability to think and work leading to vast opportunity for constructive human potential advancement.
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eeg analysis for Brainwave balancing index bbi
Computational Intelligence Communication Systems and Networks, 2010Co-Authors: Zunairah Hj Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani Abdul S Kadir, Norizam Sulaiman, Mahfuzah MustafaAbstract:The purpose of this research is to establish the fundamental Brainwave balancing index (BBI) using EEG signals. Brainwave signals from EEG were measured and analyzed using intelligent signal processing techniques and specific algorithm. Consequently, the signals were statistically correlated with established psychoanalysis techniques to produce BBI system. The result shows that the PSD analysis provides reliable BBI with 80% conformity. The fundamental findings (Brainwave balancing index and brain dominance) from this research can be served as a simple indicator of one’s thinking leading to great opportunity for positive human potential development.
A. H. Jahidin - One of the best experts on this subject based on the ideXlab platform.
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classification of intelligence quotient via Brainwave sub band power ratio features and artificial neural network
Computer Methods and Programs in Biomedicine, 2014Co-Authors: A. H. Jahidin, Mohd Nasir Taib, Md N Tahir, I M Yassin, Sahrim LiasAbstract:This paper elaborates on the novel intelligence assessment method using the Brainwave sub-band power ratio features. The study focuses only on the left hemisphere Brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the Brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
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Acute Ischemic Stroke Brainwave Classification Using Relative Power Ratio Cluster Analysis
Procedia - Social and Behavioral Sciences, 2013Co-Authors: W. R. W. Omar, A. H. Jahidin, N. Fuad, R Jailani, M. N. Taib, Rosiatimah Mohd Isa, Z SharifAbstract:AbstractThis study proposed the application of cluster analysis to classify the Brainwaves of stroke level based on the Relative Power Ratio (RPR) techniques. RPR was performed to determine the Brainwave characteristics due to group of stroke level. In this research, we determined seventy four stroke patients Brainwave activity with open eyes (OE) session. Then group them into Advance Group (AG), Intermediate Group (IG) and Early Group (EG). Simultaneously, their Electroencephalogram (EEG) was recorded from which the EEG dataset will be calculated using the RPR formula. Beta, Alpha, Theta and Delta Power Spectrum Density (PSD) are used as input for RPR. The results show that by implementing RPR technique, the pattern of group stroke level, especially in the cognitive or thinking abilities can be clearly observed. Then cluster analysis using a factorial ANOVA (analysis of variance) design with factors Group will be deployed to cluster RPR towards the corresponding group of stroke level. It can be concluded that the AG are higher in the cluster 1 for RPR Beta (RPRB) while IG and EG were grouped together because they do not differ from each other in cluster 2. For RPR Alpha (RPRA), EG are higher in the cluster 2 while AG and IG were grouped together in cluster 1. AG was found in both clusters for RPR Theta (RPRT) and AG are lower in cluster 1 for RPR Delta (RPRD). This indicates that the group stroke level can discriminate due to the Brainwaves characteristics
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Classification of Brainwave Asymmetry Influenced by Mobile Phone Radiofrequency Emission
Procedia - Social and Behavioral Sciences, 2013Co-Authors: Rosiatimah Mohd Isa, A. H. Jahidin, N. Fuad, W. R. W. Omar, M. N. Taib, Idnin Pasya, H. Norhazman, S.b. Kutty, Syed Farid Syed AdnanAbstract:AbstractA discriminant classification of human Brainwave signals influenced by mobile phone radiofrequency (RF) emission is proposed in this paper. Brainwave signals were recorded using electroencephalograph (EEG) focusing on the alpha sub-band with frequency range from 8 to 12Hz. The EEG test was divided into 3 sessions; Before, During and After with 5minutes duration for each session. Analysis involved 95 participants from engineering students. The students were grouped into 3 groups according to the side of exposure; Left Exposure (LE), Right Exposure (RE) and Sham Exposure (SE). This work suggested that RF emit by the mobile phone give several effects to Brainwave signals and there are significant different between the session of exposure. As result, the highest classification rate as high as 94.7% is achieved in session During
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Brainwave sub-band power ratio characteristics in intelligence assessment
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: A. H. Jahidin, Mohd Nasir Taib, N. Md Tahir, M. S. A. Megat Ali, Sahrim Lias, N. Fuad, W. R. W. OmarAbstract:This paper discusses on the Brainwave sub-band characteristics for different intelligence groups based on electroencephalogram (EEG) power ratio technique. The EEG datasets have been collected from 50 healthy subjects for two sessions; at relaxed, closed eye (CE) state as reference and at cognitively-stimulated state. In the stimulated state, subjects need to answer the intelligence quotient (IQ) test based on Raven's Standard Progressive Matrices (RPM). Sub-band power ratio from the two sessions were calculated and further analyzed to observe the pattern among different IQ groups. The results show that by implementing power ratio technique, the pattern of IQ groups, especially in the relaxed state can be clearly observed. It can be concluded that the value for alpha ratio is higher for high IQ group compared to low IQ group. In contrast to beta and theta ratio where high IQ groups have lower value compared to the low IQ group. This indicates that the ESD ratios can discriminate the characteristic of Brainwaves for intelligence assessment.
Zunairah Hj Murat - One of the best experts on this subject based on the ideXlab platform.
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ICCSCE - Temporal hemispheric dominance of omega-3: Measurement of theta and delta Brainwaves using EEG
2014 IEEE International Conference on Control System Computing and Engineering (ICCSCE 2014), 2014Co-Authors: Muhammad Bin Yahya, Zunairah Hj MuratAbstract:Brainwave activities were said to differ under different conditions. The main objective of this study is to find the scientific proof on the effect of omega-3 in brain development as well as Brainwave balancing index using EEG. There are four types of Brainwave bands which are Alpha, Beta, Delta and Theta. This study will only focus on Delta and Theta band waves. Twelve engineering students were selected as subjects for this study and each person was asked to consume omega-3 supplement. Brainwave activities were recorded through four stages of measurement using EEG and analysed with MATLAB software. Paired T-test analysis was used in order to find the correlation between the left and right brain before and after the consumption of omega-3. Based on the experiment, it showed that after three month consumption of omega-3 supplement, delta Brainwave was balanced but theta Brainwave was unbalanced. However, the brain balancing index shows improvement for both Brainwaves.
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Analysis of human's Brainwave pattern among active and inactive person
2012 International Conference on System Engineering and Technology (ICSET), 2012Co-Authors: Rosni Abu Kassim, Ahmad Shahran Ibrahim, Norlida Buniyamin, Zunairah Hj MuratAbstract:The paper presents a research that investigates human's Brainwave pattern among two groups of people, active and inactive. Subsequently, an experiment was conducted to explore the effectiveness of motion treatment therapy (MTT) to balance human Brainwaves. A questionnaire was used to categorize 40 male Electrical Engineering students into active and inactive groups. The questionnaire data was then analyzed to identify the capability of each group to retain memory, to focus and ability to overcome depression episodes. The goal is to prove that active person is much happier and calm. This data is then validated using analysis of the Brainwave pattern of all samples. An EEG machine was used to capture the brain waveform. It has been widely accepted that a person with a synchronized Brainwave pattern i.e., a balanced brain is usually a happy and calm person. To investigate the ability of the MTT to improve Brainwave pattern synchronization, both groups of samples then underwent 4 motion treatments and their Brainwave signals are captured. From the study, it was found that in general, active people produced more synchronized Brainwave pattern than inactive people. Comparison of Brainwave patterns before and after motion treatment indicates improvement in brain synchronization. Thus it can be assumed that to a certain extent, the motion treatment can be used to help treat inactive people.
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development of Brainwave balancing index using eeg
Computational Intelligence Communication Systems and Networks, 2011Co-Authors: Zunairah Hj Murat, Mohd Nasir Taib, Sahrim Lias, Ros Shilawani Abdul S Kadir, Norizam Sulaiman, Zodie Mohamed HanafiahAbstract:In this research, Wireless EEG equipment via Bluetooth technology named g-Mobilab was used to measure the Brainwave signals in the right and left frontal area of the brain. The recorded EEG signals were channelled into an automatic artifact removal analysis whereby signals above values of 100 micro-volts were removed by means of a program using Matlab. Consequently, Power Spectral Density techniques and specific algorithm were employed to further enhance the EEG signals. The correlation between the left and the right Brainwaves were achieved using paired T test from SPSS. The results, which are Brainwave balancing index (BBI) and Brainwave dominance, were presented via Graphic User Interface (GUI). The outcome shows that BBI system could be established using EEG signals. These findings (Brainwave dominance and BBI) could be used as a straightforward indicator of one's ability to think and work leading to vast opportunity for constructive human potential advancement.
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the effects of mobile phone usage on human Brainwave using eeg
International Conference on Computer Modelling and Simulation, 2011Co-Authors: Zunairah Hj Murat, Ros Shilawani S. Abdulkadir, Roshakimah Mohd Isa, Mohd Nasir TaibAbstract:The aim of this research is to investigate any effects of mobile phone usage on human Brainwaves using electroencephalograph (EEG) particularly on alpha wave. EEG signals were recorded from thirty samples that make calls from a mobile phone to another party without conversation. The mobile phone is strapped to the right ear. The EEG recording took place before, during and after the mobile phone calls. In addition, samples will be interviewed with questions related to the usage of hand phones prior to EEG recording. The Brainwave signals were analyzed using statistical analysis. The EEG result shows that the alpha level of the right side decreases significantly during the calls and further decreases within the period of five minutes after the calls were ended. However, the alpha level of the left side remains consistent throughout the experiment. It follows that the correlation between the left and the right Brainwaves signal decreases significantly during the calls and further decreases within the period of five minutes after calls. There is evidence that the usage of mobile phones affect the alpha Brainwaves.
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UKSim - The Effects of Mobile Phone Usage on Human Brainwave Using EEG
2011 UkSim 13th International Conference on Computer Modelling and Simulation, 2011Co-Authors: Zunairah Hj Murat, Ros Shilawani S. Abdulkadir, Roshakimah Mohd Isa, Mohd Nasir TaibAbstract:The aim of this research is to investigate any effects of mobile phone usage on human Brainwaves using electroencephalograph (EEG) particularly on alpha wave. EEG signals were recorded from thirty samples that make calls from a mobile phone to another party without conversation. The mobile phone is strapped to the right ear. The EEG recording took place before, during and after the mobile phone calls. In addition, samples will be interviewed with questions related to the usage of hand phones prior to EEG recording. The Brainwave signals were analyzed using statistical analysis. The EEG result shows that the alpha level of the right side decreases significantly during the calls and further decreases within the period of five minutes after the calls were ended. However, the alpha level of the left side remains consistent throughout the experiment. It follows that the correlation between the left and the right Brainwaves signal decreases significantly during the calls and further decreases within the period of five minutes after calls. There is evidence that the usage of mobile phones affect the alpha Brainwaves.
W. R. W. Omar - One of the best experts on this subject based on the ideXlab platform.
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assessment of acute ischemic stroke Brainwave using relative power ratio
International Colloquium on Signal Processing and Its Applications, 2013Co-Authors: W. R. W. Omar, Mohd Nasir Taib, Roshakimah Mohd Isa, R Jailani, Z SharifAbstract:This paper examines the Brainwave sub-band characteristics for three different group of stroke level based on the Relative Power Ratio (RPR) techniques. The EEG data sets have been collected from seventy four stroke patients with open eyes (OE) session. From these sessions, the sub-band RPR were calculated and further analysis is perform to determine the Brainwave characteristics due to group stroke level. The results show that by implementing RPR technique, the pattern of group stroke level, especially in the cognitive or thinking abilities can be clearly observed. It can be concluded that the value for beta ratio is higher for Advance Group (AG) compare to Intermediate Group (IG) and Early Group (EG). In contrast alpha ratio for EG have higher value compared to AG and IG stroke level. This indicates that the PSD ratios can discriminate the characteristics of Brainwaves for group stroke level assessment.
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Acute Ischemic Stroke Brainwave Classification Using Relative Power Ratio Cluster Analysis
Procedia - Social and Behavioral Sciences, 2013Co-Authors: W. R. W. Omar, A. H. Jahidin, N. Fuad, R Jailani, M. N. Taib, Rosiatimah Mohd Isa, Z SharifAbstract:AbstractThis study proposed the application of cluster analysis to classify the Brainwaves of stroke level based on the Relative Power Ratio (RPR) techniques. RPR was performed to determine the Brainwave characteristics due to group of stroke level. In this research, we determined seventy four stroke patients Brainwave activity with open eyes (OE) session. Then group them into Advance Group (AG), Intermediate Group (IG) and Early Group (EG). Simultaneously, their Electroencephalogram (EEG) was recorded from which the EEG dataset will be calculated using the RPR formula. Beta, Alpha, Theta and Delta Power Spectrum Density (PSD) are used as input for RPR. The results show that by implementing RPR technique, the pattern of group stroke level, especially in the cognitive or thinking abilities can be clearly observed. Then cluster analysis using a factorial ANOVA (analysis of variance) design with factors Group will be deployed to cluster RPR towards the corresponding group of stroke level. It can be concluded that the AG are higher in the cluster 1 for RPR Beta (RPRB) while IG and EG were grouped together because they do not differ from each other in cluster 2. For RPR Alpha (RPRA), EG are higher in the cluster 2 while AG and IG were grouped together in cluster 1. AG was found in both clusters for RPR Theta (RPRT) and AG are lower in cluster 1 for RPR Delta (RPRD). This indicates that the group stroke level can discriminate due to the Brainwaves characteristics
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Classification of Brainwave Asymmetry Influenced by Mobile Phone Radiofrequency Emission
Procedia - Social and Behavioral Sciences, 2013Co-Authors: Rosiatimah Mohd Isa, A. H. Jahidin, N. Fuad, W. R. W. Omar, M. N. Taib, Idnin Pasya, H. Norhazman, S.b. Kutty, Syed Farid Syed AdnanAbstract:AbstractA discriminant classification of human Brainwave signals influenced by mobile phone radiofrequency (RF) emission is proposed in this paper. Brainwave signals were recorded using electroencephalograph (EEG) focusing on the alpha sub-band with frequency range from 8 to 12Hz. The EEG test was divided into 3 sessions; Before, During and After with 5minutes duration for each session. Analysis involved 95 participants from engineering students. The students were grouped into 3 groups according to the side of exposure; Left Exposure (LE), Right Exposure (RE) and Sham Exposure (SE). This work suggested that RF emit by the mobile phone give several effects to Brainwave signals and there are significant different between the session of exposure. As result, the highest classification rate as high as 94.7% is achieved in session During
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Brainwave sub-band power ratio characteristics in intelligence assessment
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: A. H. Jahidin, Mohd Nasir Taib, N. Md Tahir, M. S. A. Megat Ali, Sahrim Lias, N. Fuad, W. R. W. OmarAbstract:This paper discusses on the Brainwave sub-band characteristics for different intelligence groups based on electroencephalogram (EEG) power ratio technique. The EEG datasets have been collected from 50 healthy subjects for two sessions; at relaxed, closed eye (CE) state as reference and at cognitively-stimulated state. In the stimulated state, subjects need to answer the intelligence quotient (IQ) test based on Raven's Standard Progressive Matrices (RPM). Sub-band power ratio from the two sessions were calculated and further analyzed to observe the pattern among different IQ groups. The results show that by implementing power ratio technique, the pattern of IQ groups, especially in the relaxed state can be clearly observed. It can be concluded that the value for alpha ratio is higher for high IQ group compared to low IQ group. In contrast to beta and theta ratio where high IQ groups have lower value compared to the low IQ group. This indicates that the ESD ratios can discriminate the characteristic of Brainwaves for intelligence assessment.