The performance analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification based on EEG Signal
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EDP Sciences
2021
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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 |
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K-Nearest Neighbors (K-NN) Algorithms |
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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 |