Mohammad Shahrazel Razalli
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Preferred name
Mohammad Shahrazel Razalli
Official Name
Mohammad Shahrazel , Razalli
Alternative Name
Razalli, Mohd Shahrazel
Razalli, Mohamad Shahrazel
Razalli, Mohammad S.
Razalli, M. S.
Main Affiliation
Scopus Author ID
24825300000
Researcher ID
FUH-9138-2022

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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.