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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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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EEG signal processing using deep learning for motor imagery tasks: Leveraging signal images

2025 , Saidatul Ardeenawatie Awang , Abdul Hamid Adom , Erdy Sulino Mohd Muslim Tan , Husna Najeha Amran , Arni Munira Markom

A novel approach to processing electroencephalography (EEG) signals has emerged, leveraging the utilization of signal images. The application of deep learning techniques in bypassing complex signal and image processing tasks has generated significant interest in this field. However, challenges remain in signal image processing, particularly in handling significant features and image sizes. This study presents a comprehensive investigation of EEG motor imagery signal processing, focusing on the classification of three tasks: eating, drinking, and seeking assistance. Fast Fourier Transform (FFT) is employed to extract signal image features, which are subsequently utilized in a deep learning framework. EEG data were collected from five subjects, and four transfer functions of deep learning models, namely VGG16, VGG19, ResNet50, and ResNet101, were employed for training and classification purposes. The performance of the four models was meticulously evaluated and compared. Notably, VGG16 exhibited superior performance in accurately classifying the EEG motor imagery tasks, achieving an impressive accuracy of 90%, sensitivity of 84%, and specificity of 92%. In conclusion, this study underscores the efficacy of EEG signal image processing through deep learning-based classification techniques. The findings highlight the potential of utilizing signal images in EEG analysis for motor imagery tasks, thereby contributing to the advancement of brain-computer interface technology and enhancing our understanding of neural dynamics.

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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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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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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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Traffic density estimation and mapping using IP-CCTV networks: a campus-based approach

2023 , Roy Francis Navea , John Carl Bautista , Adrian Giuseppe Francis Fernan , Zendrel Gacuya , Saidatul Ardeenawatie Awang

Closed Circuit Television (CCTV) systems are widely used in monitoring and security surveillance applications to assess status and implement measures necessary to address problems and concerns. CCTV nowadays are visible in roads for monitoring and analyzing traffic behavior and conditions. Multiple cameras are utilized to capture the different angles of the road. This is useful in improving traffic management systems, determination of road traffic density, accident reviews, and in some advanced applications, contactless apprehension. Small to medium scale community areas like industrial parks, villages, hospitals, and even academic campuses require traffic monitoring systems. In this study, a network of IP-CCTV cameras was designed to capture vehicular movement, density, and road condition in a campus setting. The network is composed of eight IP-CCTV cameras processed by four Raspberry Pi computer boards in a 2:1 camera-to-computer ratio. A graphical user interface displays the video feed of the cameras, time customizable traffic report, and the road map visualization and notifications. All computer boards can send and receive data and can create visual traffic maps displayed in the user interface. Color-coding is used in the road segments to indicate light, moderate, or heavy traffic conditions. The vehicle detection accuracy of the system is 93% while its status notification accuracy is 84%. In a campus-based application, especially those with medical and health research institutes, this model suffices its requirements in monitoring, analyzing, basis for emergency rerouting, and improvements in traffic management.

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Preliminary study on modelling and simulation of virtual reality assistive tool for autism children using gaming software

2023 , Shahrol Mohamaddan , Ting Sing Hong , Annisa Jamali , Siti Raudzah Ghazali , Saidatul Ardeenawatie Awang , Peeraya Sripian , Akihiko Hanafusa

Autism Spectrum Disorder (ASD) is an early childhood disorder that affects individual ability to interact and socialize with other people. Children with ASD have problems interacting with their peers and have difficulty to exercise their social skills. In this study, a virtual reality (VR) based assistive tool was modelled and simulated using a gaming software called Dark Basic Professional (DBPro). The assistive tool was developed to support ASD children to interact and apply the social skills. Seven tasks were designed for the VR assistive tool based on three targeted skills including facial expression recognition, reading comprehension, and task delivery. As a preliminary study, only three non-ASD children were participated in the VR assistive tool experiment to analyse the tasks. The experiment results showed that all participants successfully performed five out of seven tasks. However, all participants failed to perform the video prompting task while one participant was not able to recognize the emotion from robotics-based faces in the facial expression. The results from pulse sensor showed that the heart rate was stable during the basic experiment using VR but unstable during the applied experiment and real-world discussion. There is a need to review the overall modelling and simulation technique, and the number of participants among ASD children should be increased for the future study.

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Computed Tomography Virtual Lab Software application in Biomedical Electronic Engineering Programme at University Malaysia Perlis

2010-06-09 , Zulkarnay Zakaria , Mohd Hafiz Fazalul Rahiman , Saidatul Ardeenawatie Awang , Nur Farahiyah Mohamad , Azaian Azamimi Abdullah , Ruzairi Abdul Rahim

Computed Tomography (CT) is a medical imaging method and is among the common equipment or machine in a hospital which is vital in imaging certain parts of human body for the purpose of screening and detecting of deceases like cancers, tumors and several others by producing characteristics of the internal structure of the object such as dimensions, shape, internal defects, and density. CT is a powerful nondestructive technique for producing cross sectional image 2D or 3D depends on the technologies applied. Since its introduction in the 1970s, CT has become an important tool in medical imaging to supplement Xrays and medical ultrasonography. In University Malaysia Perlis (UniMAP), Computed Tomography and Applications has been offered as an elective course in Biomedical Electronic Engineering Programme. In this course, the student are introduced to the Computed Tomography Virtual Lab Software which has been developed by Tomographic Imaging Research Group, School of Mechatronic Engineering, UniMAP in 2009 as part of the teaching aids. Through this approached, the course are more attractive and the students are found easier in understanding the concept of basic tomography system, image reconstruction process, limitations and several possible of error sources.

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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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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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