Zuradzman Mohamad Razlan
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Zuradzman Mohamad Razlan
Official Name
Zuradzman, Mohamad Razlan
Alternative Name
Razlan, Zuradzman Mohamad
Zuradzman, M. R.
Razlan, Zuradzman M.
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Scopus Author ID
55178487200
Researcher ID
AAU-4508-2020

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  • Publication
    Simulation studies of the hybrid human-fuzzy controller for path tracking of an autonomous vehicle
    ( 2021-01-01)
    Halin H.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Haris H.
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Zunaidi I.
    ;
    Wan Azani Wan Mustafa
    Human intelligence and experience help them in making a decision and recognize a pattern. This ability enables the driver to take action even in an unexpected situation. The hybrid integration between human intelligence/experience and machine controller able to improve the autonomous vehicle path tracking capability. The path tracking capability is the main concern of the autonomous vehicle. The Fuzzy developed from the experiment’s data. The experiments (human navigation experiments) used to gather the appropriate data from humans while controlling the buggy car. Data then use to develop the membership functions for inputs and output of the Fuzzy controller. The simulation uses to study the performance of the Fuzzy controller. The recorded path tracking error from the simulations for the right and left turn maneuver is 9 m and 7.5 m, respectively.
  • Publication
    Path tracking simulation of the buggy car by using Fuzzy information of the steering wheel
    ( 2020-01-01)
    Halin H.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Haris H.
    ;
    Zunaidi I.
    ;
    Bakar S.A.
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Azani Wan Mustafa
    The steering wheel control is the method used for the navigation of an autonomous vehicle. In order to control the autonomous vehicle, the steering wheel controller must be able to adapt as the road condition and surrounding environment can change abruptly. The existed autonomous system currently in the testing phase. The system still needs to improve because there is some report regarding an accident caused by the test autonomous vehicles. The aim of this research is to implement the human driving capability into the Fuzzy controller. One of the human capabilities is the ability to make a decision based on the current situation. The fuzzy system is developed based on human driving data while controlling a buggy car. The experiments used to collect data such as position, speed, heading and steering wheel angle. Data then use to develop the membership function for the fuzzy inputs and output. The simulation is performed in order to study the performance of the Fuzzy controller. The performance of the Fuzzy controller is satisfactory and can be improved. The maximum path tracking error recorded is 9 m and 7.5 m for right and left turn simulations.
  • Publication
    Classification of Human Emotions Using EEG Signals in a Simulated Environment
    ( 2022-01-01)
    Hafiz Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Muhajir Ab. Rahim
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    The Brain-Computer Interface (BCI) is a computer-based system that acquires and analyses brain signals. The analysis of brain signals shows the physiological change that happens to the drivers. The physiological changes detected by the BCI system may not be visible to the naked eye. By using the BCI, it increases the diagnostic capability to detect the drivers' emotions. The negative drivers' emotions may cause bad decision making during driving the vehicle. The proposed method was developed to study the related emotions that occur during driving in the simulation environment. The experiments were designed in two situations, which are manual and autonomous drive. In the manual mode, the subjects will control the steering wheel and acceleration of the simulated vehicle. While in autonomous mode, all controls are disable and the subjects will experience the automatic simulation drive. The EEG data was recorded during the simulated drive (manual and autonomous). The EEG data from the subjects were then categorised into five emotions classifications.
      1  16
  • Publication
    An emotion assessment of stroke patients by using bispectrum features of EEG signals
    ( 2020-10-01)
    Yean, Choong Wen
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Murugappan M.
    ;
    Rajamanickam Y.
    ;
    Abdul Hamid Adom
    ;
    Omar, Mohammad Iqbal
    ;
    Zheng, Bong Siao
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
      4  9
  • Publication
    Design of Experiment (DOE) for the Investigation of Human Emotions while Driving in a Virtual Environment through Brain Signal (EEG)
    ( 2021-01-01)
    Hafiz Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Muhajir Ab. Rahim
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    The transition from the conventional vehicle to the autonomous vehicle is going to take place but, the acceptance of users to the autonomous vehicle still lacking. The past research more focusses on the driver attention, drowsiness, fatigue or the alertness of the driver. This research aims to study the drivers' emotions/reactions during the autonomous and manual drive in the simulated environment. The environment for the manual and autonomous drive is developed by using simulator software, Unity. This paper focus only on the experimental setup for the human emotions' detection using EEG signal during the manual and autonomous drive. The Emotiv Epoc+ use for the EEG signal acquisition. The simulated environments are displayed through a Head Mount Display (HMD). The analysis of the EEG signal which includes the pre-processing, feature extraction, and classification will be discussed in future works.
      1  17
  • Publication
    An emotion assessment of stroke patients by using bispectrum features of EEG Signals
    ( 2020)
    Choong Wen Yean
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Murugappan Murugappan
    ;
    Yuvaraj Rajamanickam
    ;
    Abdul Hamid Adom
    ;
    Mohammad Iqbal Omar
    ;
    Bong Siao Zheng
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
      20  1
  • Publication
    Investigation of the Brain Activation Pattern of Stroke Patients and Healthy Individuals During Happiness and Sadness
    ( 2022-01-01)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Murugappan M.
    ;
    Wan Azani Wan Mustafa
    ;
    Abdul Hamid Adom
    ;
    Bong S.Z.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    This study aimed to assess the emotional experiences of stroke patients and normal people using electroencephalogram (EEG) signals in happiness and sadness. The brain behaviors under both emotional states in the EEG signal were analyzed through signal processing methods. In this study, the EEG signals of normal control (NC) and stroke patients with left brain damage (LBD) and right brain damage (RBD) were analyzed through Hjorth parameters. The extracted Hjorth parameters showed significant differences between happiness and sadness in alpha, beta, and gamma frequency bands, which implied the two emotions exhibiting different brain behavior in different EEG bands. The topographic mapping of the Hjorth parameters presented different activation patterns in the subject groups, and the higher frontal activation can be observed from the NC group for both emotions. Also, the Hjorth Mobility and Complexity parameters were lower in LBD and RBD in the frontal regions of the alpha band. The significant difference channels between the emotions were analyzed by statistical analysis using ANOVA. Moreover, the features of each subject group were used for emotion classification by the application of machine learning-based algorithm. The KNN classification results achieved an average accuracy of 92.35% for NC, 90.84% for LBD, and 95.59% for RBD in classifying happiness and sadness. The emotion classification showed that the emotional dominance frequency bands were the beta and gamma bands. However, the alpha band activity showed left frontal lateralization in the NC group, while right frontal lateralization in the LBD and RBD groups suggested different brain activation of the stroke groups and the controls during happiness and sadness, which reflected the emotional impairment in stroke groups.
      2
  • Publication
    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.
      1  25
  • Publication
    Emotional states analyze from scaling properties of EEG signals using hurst exponent for stroke and normal groups
    ( 2020-01-01)
    Yean C.W.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Omar M.I.
    ;
    Murugappan M.
    ;
    Ibrahim Z.
    ;
    Zheng B.S.
    ;
    Abu Bakar S.
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Azani Wan Mustafa
    Emotion is regulated by the interconnection of the brain network. Each emotion is a different mental state, where the neuronal oscillations differ for different emotions. The EEG signal has been a useful method to analyze emotions. Furthermore, the neuronal oscillation can be observed by analyzing the scaling properties of EEG signal. In this study, the EEG signal was used as the source of emotions of stroke patients and normal subject. The Hurst Exponent (HURST) was estimated from the EEG signal to analyze the auto-correlation of the signal. The estimated HURST indicated that all emotions in this work were exhibit positive correlation in the time scale, also the neuronal oscillation for every emotions experimented were statistically different.
      2  14
  • Publication
    Hurst exponent based brain behavior analysis of stroke patients using eeg signals
    ( 2021-01-01)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Murugappan M.
    ;
    Omar M.I.
    ;
    Bong S.Z.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Wan Azani Wan Mustafa
    The stroke patients perceive emotions differently with normal people due to emotional disturbances, the emotional impairment of the stroke patients can be effectively analyzed using the EEG signal. The EEG signal has been known as non-linear and the neuronal oscillation under different mental states can be observed by non-linear method. The non-linear analysis of different emotional states in the EEG signal was performed by using hurst exponent (HURST). In this study, the long-range temporal correlation (LRTC) was examined in the emotional EEG signal of stroke patients and normal control subjects. The estimation of the HURST was more statistically significant in normal group than the stroke groups. In this study, the statistical test on the HURST has shown a more significant different among the emotional states of normal subject compared to the stroke patients. Particularly, it was also found that the gamma frequency band in the emotional EEG has shown more statistically significant among the different emotional states.
      25  1