Wan Khairunizam Wan Ahmad
Thumbnail Image
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
Universiti Malaysia Perlis
Scopus Author ID
57200576499
Researcher ID
E-6072-2011

Research Output

Filters

2
Show more
Reset filters

Settings
Now showing 1 - 2 of 2
  • 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
    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