Wan Khairunizam Wan Ahmad
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Preferred name
Wan Khairunizam Wan Ahmad
Official Name
Wan Khairunizam, Wan Ahmad
Alternative Name
Wan, Khairunizam
Ahmad, Wan Khairunizam Wan
Khairunizam, W. A. N.
Main Affiliation
Scopus Author ID
57200576499
Researcher ID
E-6072-2011

Research Output

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  • Publication
    Imaginary Finger Control Detection Algorithm Using Deep Learning with Brain Computer Interface (BCI)
    (ALife Robotics Corporation Ltd, 2022-01-01)
    Gobee S.
    ;
    Mokhtar N.
    ;
    Arof H.
    ;
    Shah N.M.
    ;
    Wan Khairunizam Wan Ahmad
    Before the advancement of deep learning technology, the brain signals are to be analysed manually by the neuroscientists on how the brain signals reacts in proportion with the human body. This process is very time consuming and unreliable. Therefore, this project aims to develop a brain signal detection system based on deep learning algorithm in response to the output of EEG device on the imagery finger movements. These fingers include thumb, index, middle, ring and little of right hand. There are 4 CNN classification models being developed in this project. They differ with each other in terms of the pre-processing requirements and the neural network architecture. The best results for offline classification obtained in this project are 69.07% and 82.83% respectively in terms of average accuracy from 6-class and 2-class tests. Moreover, this project has also developed a proof of concept for applying the trained models in online or real-time classification.
      2  6
  • 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.
      4  3
  • Publication
    Deep learning based imaginary finger control detection algorithm
    (ALife Robotics Corporation Ltd, 2022-01-01)
    Gobee S.
    ;
    Mokhtar N.
    ;
    Arof H.
    ;
    Shah N.M.
    ;
    Rajagopal H.
    ;
    Wan Khairunizam Wan Ahmad
    Conventionally, the brain signals were analysed manually by the neuroscientists on how the brain signals reacts in proportion with the human body. However, this process is very time consuming and unreliable. Therefore, we have proposed a brain signal detection system based on deep learning algorithm in response to the output of EEG device on the imagery finger movements. These fingers include thumb, index, middle, ring and little of right hand. In this study, 4 Convolutional Neural Network (CNN) classification models were developed. These 4 CNN models are different in terms of the pre-processing requirements and the neural network architecture. The best results for offline classification obtained in this project are 69.07% and 82.83% respectively in terms of average accuracy from 6-class and 2-class tests. Moreover, this project has also developed a proof of concept for applying the trained models in online or real-time classification.
      3  4