Saidatul Ardeenawatie Awang
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Saidatul Ardeenawatie Awang
Official Name
Saidatul Ardeenawatie, Awang
Alternative Name
Saidatul, Ardeenawatie
Awang, S. A.
Awang, Saidatul Ardeenawatie
Awang, Saidatul Ardeenaawatie
Awang, Saidatul Ardeenawatie Binti
Awang, Saidatul Ardeenawatiebinti
Saidatul, A.
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Scopus Author ID
57205231792
Researcher ID
CCJ-6771-2022

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Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals

2021-11-25 , Nawayi S.H. , Vikneswaran Vijean , Ahmad Faizal Salleh , Rashid R.A. , Planiappan R. , Lim C.C. , Fook C.Y. , Saidatul Ardeenawatie Awang

Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.

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Detection of Parkinson's Disease (PD) Based on Speech Recordings using Machine Learning Techniques

2020-12-20 , Azian Azamimi Abdullah , Norazman N.N. , Wan Khairunizam Wan Ahmad , Saidatul Ardeenawatie Awang , Jian F.W.

There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.

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Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method

2023-01-01 , Mazlan M.R.B. , Abdul Syafiq Abdull Sukor , Abdul Hamid Adom , Jamaluddin R.B. , Saidatul Ardeenawatie Awang

Undergraduate students experience several changes and face various problems during their time transitioning from adolescence to adulthood. One of the issues during this time is a mental stress disorder. Stress burdens the students either through mental or physical capabilities. The common method of determining stress includes physical examination and clinical diagnosis. However, the method is subjective and time-consuming as doctors need to make sure that their diagnosis is accurate. Thus, the severity of the stress stages could not be easily determined. A new method using machine learning-based algorithms coupled with EEG devices promises to overcome the issues with the current approaches. This paper presents an investigation using machine learning techniques based on Principal Component Analysis (PCA) which allows for the reduction in the dimensionality of datasets to enhance their interpretability while minimizing information loss. The pre-processed EEG data and PCA-based EEG data were compared and analyzed using three machine learning classifiers such as K-Nearest Network (KNN), Naive Bayes (NB) and Multilayer Perceptron (MLP). The results indicate that KNN demonstrated the highest average classification accuracy of 99%, while the other approaches mentioned above averaged around 50% and 80% for NB and MLP respectively. This investigation shows that the KNN classifier is most suitable for the proposed approach.

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Development of neurometric acute stress assessment based on EEG signals

2014 , Saidatul Ardeenawatie Awang

Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In contrast, Distress or negative stress can devastate a person by creating depression and damage the quality of life. It is essential to comprehend and to figure out the state of current stress in numerical index. The development of a reliable data acquisition protocol is a crucial part to elicit mental stress in different level of stress. In this study, some modification on the existing Mental Arithmetic Task (MAT) has been made to ensure the designed protocol is capable to induce the different intensity of stress such as low, moderate and high. The dynamical excitation protocol and time pressure concept are proposed in this work. There are three validation methods have been used, namely, K Nearest Neighbor (KNN), Alpha Brain Asymmetry and statistical analysis (Paired T-test). As a result of this study, it was found that the proposed experimental protocol is comparable as the verification has been made with the following: (i) The t-test result based on physiological changes during pre and post experiment were found to be statistically significant (p<0.01) (ii) The mean value of Alpha Brain Asymmetry are comparable and have a potential to discriminate between levels and (iii) the classification accuracy of 84% confirmed that the proposed protocol have potential in classifying the mental stress level. Besides that, the preprocessing technique applying elliptic filters with 256 data per frame is the most suitable technique. Five types of spectral estimator (Welch, Burg, Yule Walker, Modified Covariance and Multiple Signal Classification) based feature extraction is performed on the normalized signals. The extracted features are cross validated using 10-fold cross validation and classified using KNN and have been proved using statistical analysis (ANOVA). The maximum mean classification rate of 86.75% is achieved using Modified Covariance feature derived from alpha waves using KNN. Besides that, this study found that F3 and F4 are the most informative electrodes with the classification rate of 93.50%. Last but not least, a new algorithm has been proposed based on the more established index, Alpha Asymmetry Score (AAS), as a reference. Modifications have been made in term of the frequency band as a variable in the stress index. The classification accuracy of the proposed Stress Asymmetry Score (SAS) is approximately 96% which is 10% higher than AAS. The development of the stress index promises new era of stress brain related research for future people’s benefit.

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Changes on EEG Power Spectrum with Carbohydrate Mouth Rinsing

2023-01-01 , Kamaruddin H.K. , Bakar A.H.A. , Zainuddin N.F. , Mohammad Shahril Salim , Saidatul Ardeenawatie Awang

Carbohydrate (CHO) mouth rinse has been shown to activated brain regions via oral receptors that related to rewards and motor control that can enhance exercise performance. The objective of this study was to examine the effect of CHO mouth rinsing on electroencephalography (EEG) power spectrum responses. Ten recreational athletes performed a mouth rinsing for 10 s with CHO or placebo (PLB) solution on seated position, eyes closed and in air conditioning room. The EEG was measured during the initial mouth rinsing with 14 electrodes attached on the participant’s scalp. The EEG activity included alpha, beta, delta, and theta power increased following mouth rinsing (CHO and PLB). However, a significant alpha power was observed with CHO when compared to PLB mouth rinsing on frontal and temporal regions (p = 0.003). No significant differences within beta, delta, and theta power for both solution comparisons, respectively (p > 0.05). The results of this study demonstrate that brain activity may be related to the presence of CHO mouth rinsing. The changes in cortical responses particularly in alpha power may influence the increased of arousal and motivation level.

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Biomechanical and ergonomics study of manual material handling during team lifting activity

2023-02-21 , Latib N.L.M. , Mohammad Shahril Salim , Shahriman Abu Bakar , Ahmad Faizal Salleh , Nurhidayah Omar , Hamzah Abdul Hamid , Saidatul Ardeenawatie Awang , Kamaruddin A.

This project focused only on the joint contribution and ground reaction force that took place during the team lifting activity made up of two people. This present study hypothesized that the workplace variables such as the weight of loads, the height of load to be lifted and gender would affect the kinetics and kinematics variables. Eight healthy participants (BMI: 18.5 till 24.9 kg/m2) divided into four team where there are two groups of male and two groups of females with two individuals in each team have performed asymmetric lifting task under four different conditions which are two weights of loads (5 kg and 15 kg) and two level of lifting heights (participant knuckle and elbow height from ground). There are five Oqus cameras motion capture system (Pro Reflex infrared, Qualysis) to capture the participant motion, Qualisys Track Manager (QTM) software had been used to label the markers on participant body while the force plate had been used for data collection of ground reaction force throughout the lifting activities. The data collected from QTM converted into C3D file to be used in Visual 3D software to do bone modelling and analysis on ground reaction force, joint angle and joint moment. The results show that here was a statistically significant interaction between the effect on gender and load on joint angle, p=0.001 for hip. However, there is no statistically significant interaction between gender and load on right and left knee angle. Besides the two-way ANOVA was conducted that examine the effect of gender and load on joint moment. Thus, there is a statistically significant interaction between the effect on gender and load on joint angle, p=0.001 for all joint moment at both elbow and knuckle height. In term of ground reaction force, there was a statistically significant interaction effect between gender and load on the combined dependent variable during to-lift phase at in lifting phase position at elbow height and knuckle height, p=0.001 when using two-way MANOVA. Based on the results of this study, it was concluded that hip joint angle, hip and knee joint moment affected by gender and load while for ground reaction force are influenced by the variables of lifting height, lifting stage, gender and weight of loads.

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Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

2024-06-01 , Abdul Hafiz Abd Halin , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Muhajir Ab. Rahim , Zuradzman Mohamad Razlan , Shahriman Abu Bakar , Saidatul Ardeenawatie Awang

The Brain-Computer Interface (BCI) is a field of research that studies the EEG signal in order to elevate our understanding of the human brain. The applications of BCI are not limited to the study of the brain wave but also include its applications. The studies of human emotions specific to the vehicle driver are limited and not vastly explored. The EEG signal is used in this study to classify the emotions of drivers. This research aims to study the emotion classifications (surprise, relax/neutral, focus, fear, and nervousness) while driving the simulated vehicle by analyse the EEG signals. The experiments were conducted in 2 conditions, autonomous and manual drive in the simulated environment. In autonomous driving, vehicle control is disabled. While in manual drive, the subjects are able to control the steering angle, acceleration, and brake pedal. During the experiments, the EEG data of the subjects is recorded and then analyzed.

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Comparison between predicted results and built-in classification results for brain-computer interface (BCI) system

2021-05-03 , Ong Z.Y. , Saidatul Ardeenawatie Awang , Vikneswaran Vijean

Brain-computer interface (BCI) system is a system of receiving information and transferring responses by communication between a computer and human brain. BCI system acts as assistive device to help the severe motor disabilities patients to live like a normal human being. Classification results used to validate the performances of BCI system. Several classification methods have been used in BCI system. However, previous researchers did not compare the classification results with predicted results. In this study, the predicted results were calculated from the questionnaire which collected from participants after completed the experiments. These predicted results were used to compare with the results from classification learner tool. The built-in classification methods included decision tree, support vector machine (SVM), k-nearest neighbor (KNN) and ensemble classifiers. Based on the results, the average difference of predicted results and built-in classification results for cubic SVM is the smallest which is 2.41% and 1.81% for motor imagery 1 and motor imagery 2 respectively. This finding shows that the cubic SVM classifier can detect the mistake that did by the subjects during the experiment.

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Detection of Parkinson’s Disease (PD) based on speech recordings using machine learning techniques

2020 , Azian Azamimi Abdullah , Nurul Nurain Norazman , Wan Khairunizam Wan Ahmad , Saidatul Ardeenawatie Awang , Foong Wei Jian

There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.

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