Publication:
The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

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cris.author.scopus-author-id 57200078638
cris.author.scopus-author-id 36170326400
cris.author.scopus-author-id 35760261900
cris.author.scopus-author-id 24825300000
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department e4aaaf69-3436-4dcd-ab15-19828fa5a557
cris.virtualsource.department 17c75cef-7b83-41f0-8510-45f2999c38ff
cris.virtualsource.department 39d72a1b-5c49-42aa-b1c2-165e99222949
dc.contributor.author Nurul E’zzati Md Isa
dc.contributor.author Amiza Amir
dc.contributor.author Mohd Zaizu Ilyas
dc.contributor.author Mohammad Shahrazel Razalli
dc.date.accessioned 2025-01-13T13:20:39Z
dc.date.available 2025-01-13T13:20:39Z
dc.date.issued 2017-12-11
dc.description.abstract 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.
dc.identifier.doi 10.1051/matecconf/201714001024
dc.identifier.scopus 2-s2.0-85039152282
dc.identifier.uri https://hdl.handle.net/20.500.14170/13052
dc.language.iso en
dc.relation.funding Ministry of Higher Education, Malaysia
dc.relation.grantno 9003-00525
dc.relation.ispartof MATEC Web of Conferences
dc.relation.ispartofseries MATEC Web of Conferences
dc.rights open access
dc.title The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.volume 140
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.citation.number 01024
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person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
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person.identifier.scopus-author-id 57200078638
person.identifier.scopus-author-id 36170326400
person.identifier.scopus-author-id 35760261900
person.identifier.scopus-author-id 24825300000
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