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The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal

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Main Authors: Nurul E’zzati, Md Isa, Amiza, Amir, Mohd Zaizu, Ilyas, Mohammad Shahrazel, Razalli
Other Authors: nurulezzati@studentmail.unimap.edu.my
Format: Article
Language:English
Published: EDP Sciences 2021
Subjects:
K-Nearest Neighbors (K-NN)
Algorithms
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69244
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spelling my.unimap-692442021-01-06T00:49:33Z The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal Nurul E’zzati, Md Isa Amiza, Amir Mohd Zaizu, Ilyas Mohammad Shahrazel, Razalli nurulezzati@studentmail.unimap.edu.my K-Nearest Neighbors (K-NN) Algorithms Link to publisher's homepage at https://www.matec-conferences.org/ 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. 2021-01-06T00:49:33Z 2021-01-06T00:49:33Z 2017 Article MATEC Web of Conferences, vol.140, 2017, 6 pages https://doi.org/10.1051/matecconf/201714001024 2261-236X (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69244 en 2017 International Conference on Emerging Electronic Solutions for IoT (ICEESI 2017); EDP Sciences
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic K-Nearest Neighbors (K-NN)
Algorithms
spellingShingle K-Nearest Neighbors (K-NN)
Algorithms
Nurul E’zzati, Md Isa
Amiza, Amir
Mohd Zaizu, Ilyas
Mohammad Shahrazel, Razalli
The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
description Link to publisher's homepage at https://www.matec-conferences.org/
author2 nurulezzati@studentmail.unimap.edu.my
author_facet nurulezzati@studentmail.unimap.edu.my
Nurul E’zzati, Md Isa
Amiza, Amir
Mohd Zaizu, Ilyas
Mohammad Shahrazel, Razalli
format Article
author Nurul E’zzati, Md Isa
Amiza, Amir
Mohd Zaizu, Ilyas
Mohammad Shahrazel, Razalli
author_sort Nurul E’zzati, Md Isa
title The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
title_short The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
title_full The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
title_fullStr The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
title_full_unstemmed The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
title_sort performance analysis of k-nearest neighbors (k-nn) algorithm for motor imagery classification based on eeg signal
publisher EDP Sciences
publishDate 2021
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69244
_version_ 1698698578478759936
score 13.222552

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