Search

Cymbopogon oil is derived from the Cymbopogon species which are lemongrass and citronella. The essential oil is primarily used as a mosquito repellent as a pesticide, and also has other insecticidal, acaricidal and herbicidal activity. It is not considered harmful to humans and pets but may cause skin irritation. The aim of this paper is to model the Cymbopogon oils species using k-Nearest Neighbours (k-NN). The work consists of Cymbopogon oils data extraction using GC and GC-MS machine for compound identification. After that, a quick observation on the major compound in the samples as significant compound identification was done. Then, pre-processing data was performed which consist of data randomization and data splitting into training and testing dataset with the ratio of 80%:20%, respectively. Next, the k-NN modelling was carried out using training dataset and also applying Euclidean as distance metric. It was followed by the tested of developed k-NN using testing dataset. The result showed that k=1 and k=2 achieved 100% accuracy for both testing and training dataset. This proven that k-NN modelling in this study was able to classify the Cymbopogon oil compounds according to its species which are citronella and lemongrass.

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased.

Essential oils are very valuable natural resources and considered as secondary metabolites. They are produced from several parts of aromatic plant by using different type of extraction techniques. Each technique produced slightly different output oil yield and smell however they produced the same major chemicals compound markers when they are analysed using chemical analysis and profiling technique. Pure essential oils are known to have very strong odor and there are several techniques used to differentiate the volatile odor generated. In this study, Electronic Nose (E-Nose) technology is used to distinguish the smell among 8 samples selected within the same Zingiberaceae family. Their pattern recognition profiles were examined by statistical analysis using Discriminant Factorial Analysis (DFA). The result shows that the E-Nose technology combined with DFA were successfully discriminating all 8 samples within the same family with significant p-values < 0.05 across all samples and 100% recognition value.

The aim of this work is to analyze asymmetry pattern of resting brainwave for different intelligence levels. EEG signals were measured from fifty samples and three IQ levels were established from Raven's Progressive Matrices. Asymmetry scores for alpha and beta waves were computed by subtracting ln-transformed ESD of the left from right hemisphere. The study elucidate that individuals in high IQ level exhibit a balanced brain with smaller asymmetry score for alpha and beta waves compared to medium and low IQ levels. Meanwhile, the medium and low IQ levels exhibit unbalanced brain alpha and beta activity with greater asymmetry values.

Learning styles (LS), being one of the important attributes of a learner's profile, are relevant to different aspects of teaching and learning such as the learner's achievement and motivation. Equally important is the personality traits of ‘Openness’, which relate positively to knowledge and skill acquisition, thus making them relevant to learning and learners differences. Recognizing the importance of LS and Openness in profiling learners, the researchers carried out this study to examine the relationship between these two factors using a novel method based on Electroencephalogram (EEG) technology. In this research, Kolb's Learning Style Inventory (KLSI) was used to determine 131 participants’ LS: Diverger, Assimilator, Converger or Accommodator . The EEG technology was used to record the participants’ brain signals (with their eyes closed) to generate the dataset of EEG Beta band of baseline condition. Later, the dataset was processed and classified based on the LS using the 2-Step Cluster Analysis . The result showed that the brain signals could be processed effectively to classify the participants’ LS. More importantly, among the LS studied, convergers and assimilators were observed to have positive and strong relation with Openness. Between the two learning styles, assimilators were found to have stronger relation with Openness than convergers.

Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50% respectively.

The importance to recognize a learner's Learning Style (LS) is ever-essential as to substantiate success in a teaching and learning process. At the same time, the learner's IQ and personality traits such as Stress also being actively investigated in educational research as educationists consistently attempted to understand learners in a more adept way. Nevertheless, the effort was usually confined to psychoanalysis test. With the emergence of Electroencephalography (EEG) technology, learner's brain characteristics could be accessed directly and the outcome may well hand-in-hand supported the conventional test. In this study, the participants (n= 80) are grouped to the LS of Diverger, Assimilator, Converger or Accommodator using the Kolb's Learning Style Inventory (KLSI). Subsequently, their brain signals were then recorded using EEG at resting baseline state of Open Eyes and Closed Eyes. A statistical tool of SPSS 16 was used for data analysis purposes. Using the Two Step Cluster analysis, the participants’ EEG datasets were 100% classified to the corresponding LS. Then, EEG Alpha band was selected to link between LS, IQ and Stress. The study concluded that Diverger is the LS with highest IQ while Converger and Diverger are the LS that prone to Stress.

This article presents the use of Z-score in assessing the significant chemical compounds extracted by head space solid phase microextraction (HS-SPME) and gas chromatography – mass spectrometry (GC-MS) analysis of an agarwood oil obtained from Melaka, Malaysia. Two types of SPME fiber; polydimethylsiloxane (PDMS) and divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) were used. During the extraction analysis, the results showed that at least 27 and 29 compounds were identified using PDMS and DVB-CAR-PDMS fiber, respectively. DVB-CAR-PDMS fiber was found to be more efficient in terms of selectivity of compounds extraction. The application of Z-score showed that eight and eleven marker compounds were determined in PDMS and DVB-CAR-PDMS fibers, respectively. 4-Phenyl-2-butanone, a-guaiene, β-agarofuran, a-bulnesene, a-agarofuran and 10-epi-g-eudesmol were some of the compounds selected and were often reported significantly in agarwood oils as key odor compounds. The information gathered will be used for compound selection towards grading of agarwood oils by sensor technology.

This paper describes the performance of a commercial AlphaMOS 4000 Electronic Nose coupled with Discriminant Factorial Analysis (DFA) as statistical tool used in discriminating the differences between pure and mixture agarwood oils by their volatile properties. The proposed techniques in this paper for testing and evaluating the capability of E-Nose for classifying and testing two different groups pure and mixtures Agarwood oil is successfully conducted. E-Nose coupled with DFA as a statistical tool is capable to discriminate ten oils from two separate groups pure and mixtures by their different odor uniqueness with significant p-value < 0.05 and both groups are recognized as different samples with 100% accuracy. Lastly, for selected unknown sample projection, the projection accuracy is 90%.

Advancement in Neuroscience namely EEG technology had been serving in education-related research with immense contributions. On the other hand, Learning Style (LS) had emerged as an important study in education frontier. In this research, the classification of participants (N=131) LS is implemented using EEG Beta Summative Power Spectrum Density (PSD) and Kolb's Learning Style Inventory questionnaire. The LS clustering process with EEG Beta Summative PSD dataset is fulfilled using SPSS Two-steps Cluster Analysis tool. The findings established that EEG Beta Summative power had been successful in classifying the LS with high percentage obtained. Meanwhile, the classification dissimilarity caused by EEG Beta Summative PSD in different construct was considered as minimal and Accommodators had been discovered as the best LS classified using EEG Beta Summative PSD.

Learning Style (LS) had been drawn as one of the important domains in a learner's profile. It was considered relevance to different aspects of teaching and learning such as achievement and motivation. On the other hand, Openness has been interpreted as a major trait which linked to knowledge and skill acquisition which linked to learner differences. In this research the relation between LS and Openness is studied as to underline a substantive learners' profile. Kolb's Learning Style Inventory (KLSI) had been employed to determined participants' (N= 131) LS of Diverger, Assimilator, Converger or Accommodator. Then, Electroencephalogram (EEG) technology was utilized to gather their brain signals. The signals dataset of Alpha band is selected, processed and classified towards the corresponding LS using SPSS 2-Step Cluster Analysis. The result shown 100% classification of brain signals towards LS achieved while Accommodator found as the LS that related to Openness.

One of the key factors to understand and find solutions for any challenge related to Water Distribution System(WDS) is begin from understanding the behaviors of its variables under specific circumstances, in this paper various experiments are conducted to figure out and analyze the effects of leakage on the variables of WDS, i.e. pressure, pipe volume, velocity, water demands and flow. The results of these experiments showed that the most affected variables when the WDS suffer from leakage is the pressure, followed by flow, while the least affected variable is velocity.

EEG signal research had been studied massively in such balanced brain and IQ applications. This paper focuses on correlation between balanced brain and Intelligence Quotient (IQ) applications. At first, the raw EEG signals from both applications need to pre-process to remove artefact and unwanted frequency. Then, the EEG signals will go through statistical processes which are Scatterplot and Correlation test. As a result, there is correlation between the balanced brain and IQ application with strong and significant Pearson correlation.

The term intelligence is associated in many areas such as linguistic, mathematical, music and art. In this paper, Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The EEG signals are then used to form the spectrogram images, from which a large data of Gray Level Co-occurrence Matrix (GLCM) texture features were extracted. Then, Principal Component Analysis (PCA) is used to reduce the big matrix, and is followed with the classification of the EEG spectrogram image in IQ application using ANN algorithm. The results are then validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN is able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE.

Sub-band spectral centroid (SC) has been widely applied in audio and speech processing field. This paper highlights the SC feature as a new approach to evaluate human intelligence quotient (IQ). The study focuses on resting EEG of the left brain hemisphere. The SC feature is derived from Discrete Fourier Transform (DFT) of electroencephalogram (EEG) signals. Sub-band SCs are obtained for delta, theta, alpha and beta frequency bands. IQ scores from the Raven Progressive Matrices (RPM) have been utilized to categorize dataset into three distinct groups. The SC features are then evaluated for significant pattern among the different intelligence levels. Results on theta and beta sub-bands indicate a trending pattern. Hence by implementing SC features of the theta and beta sub-bands, distinct IQ groups can be recognized.

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.

Stress can be perceived as the reaction of the body towards observed mental, emotional and physical distress. It is highly curable if it is detected and managed early. There are various methods in relieving anxiety and stress such as meditation. However, to some people, this act is hard to perform. Therefore, they switch to another therapy which is easier and simpler but deliver similar side effects as meditation i.e. brainwave entrainment. Binaural beat is one type of such brainwave entrainments, in which two signals with different frequency are presented to each ear simultaneously. In regards to this, our research work focuses on studying the effects of binaural-beats to people with stress, specifically the effects to their EEG signals. In such context, this paper presents the results collected from the experiments performed on a number of participants. EEG signals of the participants are recorded in three different states namely the initial state, after stress induction and after binaural-beats entrainment. The Alpha Symmetry method has been employed in analyzing the results especially to study the effect of stress induced and binaural beats entrainment on the participants' alpha waves.

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.

This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%.

This paper investigates the difference between the left and the right brainwaves for delta and theta frequency band after horizontal rotation (HR) intervention using electroencephalography (EEG). The EEG signals were captured from the samples before and after under-going HR. The artifact of the EEG signals was removed automatically by means of a program designed to eliminate data above 100 micro volts. Then the Power Spectral Density (PSD) was applied to the artifact removed data and comparison was carried out to determine the better method and the effects of HR intervention. MATLAB and Paired T-test analysis from SPSS was used and graphs were plotted to show the correlation between the left and right brainwave before and after HR involvement. In conclusion, by applying PSD to the artifact removed signals, results improved significantly. It also shows that the correlation of the left and the right brainwaves had improved for delta and theta frequency bands after HR.

This paper presents a results of designing an intelligent system to evaluate human stress level using Electroencephalogram (EEG) signals and Psychoanalysis tests. The questionnaires for Psychoanalysis tests were created based on Cohen's Perceived Stress Scale (PSS). EEG signals were captured using wireless EEG equipment. The Graphical User Interface (GUI) for the Psychoanalysis tests and EEG signals were created. The system was evaluated for 12 healthy subjects (7 females and 5 males). The results show that the intelligent system able to display the stress score, stress level and dominant index of EEG signals simultaneously. Thus, users can use the results of the system to take necessary action in order to improve their lifestyle.

Learning style had become an importance researched subject for understanding how individual learns. Apart from that, personality traits such as emotions, moods and self-motivation of the learners were also being actively studied. In this research, the learners' learning style and their personality traits were investigated by Electroencephalogram (EEG) Frontal Asymmetry. First, The participants (N=41) learning styles were classified using Kolb's Learning Style Inventory (KLSI) which grouped them into either Diverger, Assimilator, Converger or Accommodator style. Subsequently, their brain signal was recorded using EEG and the Frontal Asymmetry was determined using the Asymmetry Relation Ratio (ARR) formula. The result shows that Converger and Diverger were related to Right Frontal Asymmetry while Assimilator and Accommodator were associated with Left Frontal Asymmetry.

This study highlighted the use of Cluster analysis approach to classify the participants' Learning Style (LS) based on the EEG brain asymmetry (BA) dataset. BA is importance to indicate brain activity in both right hemisphere (RH) and left hemisphere (LH). The RH and LH dominant states are closely related to human learning traits such as Attention, Perception and Emotions. In this research, we determine the LS of 41 participants using Kolb's Learning Style Inventory (LSI). The LSI will group them into the LS of either Diverger, Assimilator, Converger or Accommodator. Simultaneously, their Electroencephalogram (EEG) is recorded from which the BA will be calculated using the Asymmetry Relation Ratio (ARR) formula. The Alpha and Beta Energy Spectral Density (ESD) are used as input for the ARR. Finally, the SPSS 2Steps cluster analysis will be deployed to classify the BA towards the corresponding LS. The result obtained shown that the classification of each LS is achieving 100% accuracy. We also managed to specify the significant state of LH or RH dominant for each LS.

This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) clasifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.

In Electroencephalography (EEG) research, the analysis using its time or frequency signals are very popular. However, it has been shown elsewhere, that any feature rich signals can be examined using time-frequency components. This paper proposes a new technique of extracting Gray-level Co-occurrence Matrices (GLCM) texture via time-frequency analysis of EEG signals. The output of this technique produces a big feature matrix and it is reduced by applying Principal Components Analysis (PCA). The results of this experiment shows that EEG signals can be analysed or described using five major components of the GLCM.

The learning style eloquence had garnered increase influence in education field. Simultaneously educationist also taking great interest on blending neuroscience research in their work, hence the Brain-based Learning and Brain-based Education are among the terms which had been geared into limelight recently. By taking into account, that learners' learning style is crucial in succeeding the learning process and the importance of brain as “The Organ of Learning”, we believe that the brain's fingerprint should be put under the research radar in order to discover the reciprocal relation between both entities. In this research, we used electroencephalography (EEG) technology to record a brain signal of our participants (N=41) at resting baseline state of Eyes-Open and Eyes-Closed. Prior to that, we deployed the Kolb's Learning Style Inventory to conduct a learning style classification onto them. By using a cluster analysis approach which examined the brain signals Centroids, the learning style are successfully classified into a 4 unique clusters which put a way forward towards other related classification work in future.

Study on the affiliation of the EEG alpha waves and theta waves during relaxation state and IQ test state from sixteen college students has been carried out in this paper. The alpha waves and theta waves are very much related to the relaxation and concentration respectively. By using data segmentation and linear regression techniques, power changes in alpha band and theta band are plotted. It is revealed that the alpha band relative power values reduced when the IQ test session is held and it is found that the Index 6 from the IQ group produced highest response of alpha and theta mean power comparing to other IQ Indexes. It is shown that the relative power segmentation techniques in conjunction with linear regression are able to shows significant dissimilarity among the IQ Index group by relating relaxation and IQ test experiments.

This paper presents EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. The study involved 51 subjects (27 males and 24 females) for Close-eye state (do nothing) and 50 subjects (21 males and 29 females) for Open-eye state (perform IQ test). The subjects then were categorized into 2 groups for all EEG frequency bands (Delta, Theta, Alpha and Beta) by using EEG Asymmetry technique. The negative asymmetry was labelled as Stress group and positive asymmetry was labelled as Non-Stress group. The data in each group in term of Energy Spectral Density (ESD) were normalized by using Z-score technique to produce an index to each asymmetry group. Next, the Spectral Centroids techniques were applied to each group and EEG frequency bands to obtain Centroids values. Since there were 2 asymmetry groups per EEG frequency bands, a total of 8 Centroids values were produced for each cognitive states. The plot of Centroids for both cognitive states showed some unique patterns related to stress.

The main focus of this research is to observe EEG pattern of smokers on the Theta and Delta Frequency Bands. 8 male smokers and 8 non-smokers were sampled in this study. EEG data was recorded for 3 minutes. It was found that, there some difference in the Alpha and Beta Frequency bands for smokers compared to non-smokers EEG pattern.

This research investigates the effects of five sessions of Horizontal Rotation (HR) on human brainwaves synchronization using EEG. EEG signals were captured from 42 participants before and after undergoing HR using two-channel bipolar connection in a controlled environment. The signals were filtered and classified into the four frequency bands; Delta, Theta, Alpha and Beta. Graphs were plotted and paired T-test analysis was used to demonstrate the correlation between left and right brainwaves before and after HR to verify brainwave synchronization. It was observed that after five sessions of HR, brainwaves were more synchronized for all frequency bands with highest increment of 37% in Delta band while the lowest increment is at 6% for Theta band. Thus, there was evidence that HR could synchronize brainwaves.

This paper presents analysis of brainwave dominant after horizontal rotation (HR) intervention using EEG. It was conducted by interviewed all samples. Then, EEG signals were captured from the samples before and after under-going three sessions of HR. Basically, brainwave has four frequency bands which are Alpha, Beta, Theta and Delta. However, this paper will only focus on theta and delta. Paired T-test analysis was used and graphs were plotted to show the correlation between the left and right brainwave before and after HR intervention to verify brainwave dominant. It was observed that, after HR for all three sessions most of brainwaves were balanced but still some of brainwaves signals show unbalanced condition.

This research focuses on the correlation between body mass index (BMI) and brainwave activity measured using EEG, concentrating on Alpha and Beta waves. BMI is a measurement of body fat based on height and weight. Using BMI as reference, all 63 samples were given interviewed and then categorized into 3 groups before undergoing EEG recording. The groups are underweight (index ≪20), ideal weight (index 20–24) and overweight (index ≫24). The brainwaves data were analyzed using paired T test from SPSS software. It was observed that there were higher correlations between BMI and brainwave pattern for overweight category as compared to the other two. Generally, it can also be concluded that ideal weight participants have less stress than the others do.

The main focus of this research is to analyze EEG signal of smokers after undergoing Horizontal Rotation (HR) treatment. 21 male smokers were sampled in this study. EEG data was captured before and after HR for 5 sessions. In addition, some demographic data were gathered from the samples to check their smoking habits. It was found that, HR significantly improves the Alpha band that could reduce the smoking habit.

Agarwood oils are extracted from low grade agarwood chips which are commonly processed via distillation. They can be obtained from various Aquilaria species such as A. malaccensis, A. hirta and A. crassna. This viscous oil is called as pure agarwood essential oil (100% of extracted material). Commonly, the oils are releasing long-lasting woody, warm and smoky scent due to slow release when applied to the skin. The blending of a small amount of this precious agarwood oil with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang can give rich, complex and pleasant oils. Agarwood oil acts as a base note to fix the perfume odour comprising of middle and top notes. Currently, there is no scientific technique used to differentiate pure and mixture agarwood oils and usually, it is based on the different results and inconsistency from traders and buyers agreement. Thus, as part of our work on the authentication of agarwood oils from Malaysia, we propose a method to discriminate pure and mix agarwood oils via electronic nose (E-Nose) coupled with k-NN kfold classifier with accuracy percentage improvement from 90% to 100% accuracy compared to the standard manufacturer statistical tool Discriminant Factorial Analysis (DFA).