Mohd Zaizu Ilyas
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Mohd Zaizu Ilyas
Official Name
Mohd Zaizu , Ilyas
Alternative Name
Ilyas, M. Z.
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Scopus Author ID
35760261900
Researcher ID
CVJ-8385-2022

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  • Publication
    The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal
    ( 2017-12-11)
    Nurul E’zzati Md Isa
    ;
    Amiza Amir
    ;
    Mohd Zaizu Ilyas
    ;
    Mohammad Shahrazel Razalli
    Most EEG-based motor imagery classification research focuses on the feature extraction phase of machine learning, neglecting the crucial part for accurate classification which is the classification. In contrast, this paper concentrates on the classifier development where it thoroughly studies the performance analysis of k-Nearest Neighbour (k-NN) classifier on EEG data. In the literature, the Euclidean distance metric is routinely applied for EEG data classification. However, no thorough study has been conducted to evaluate the effect of other distance metrics to the classification accuracy. Therefore, this paper studies the effectiveness of five distance metrics of k-NN: Manhattan, Euclidean, Minkowski, Chebychev and Hamming. The experiment shows that the distance computations that provides the highest classification accuracy is the Minkowski distance with 70.08%. Hence, this demonstrates the significant effect of distance metrics to the k-NN accuracy where the Minknowski distance gives higher accuracy compared to the Euclidean. Our result also shows that the accuracy of k-NN is comparable to Support Vector Machine (SVM) with lower complexity for EEG classification.
  • Publication
    Deep-Learning Assisting Cerebral Palsy Patient Handgrip Task Translation
    ( 2021-07-26)
    Fazrul Faiz Zakaria
    ;
    Mohd Nazri Mohd Warip
    ;
    Phaklen Ehkan
    ;
    Muslim Mustapa
    ;
    Mohd Zaizu Ilyas
    An electro-encephalography (EEG) brain-computer interface (BCI) can provide the brain and external environment with separate information sharing and control networks. EEG impulses, though, come from many electrodes, which produce different characteristics, and how the electrodes and features to enhance classification efficiency have been chosen has become an urgent concern. This paper explores the deep convolutional neural network architecture (CNN) hyper-parameters with separating temporal and spatial filters without any pre-processing or artificial extraction processes. It selects the raw EEG signal of electrode pairs over the cortical area as hybrid samples. Our proposed deep-learning model outperforms other neural network models previously applied to this dataset in training time (∼40%) and accuracy (∼6%). Besides, considerations such as optimum order for EEG channels do not limit our model, and it is patient-invariant. The impact of network architecture on decoder output and training time is further discussed.
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