Classification of multichannel EEG signal by single layer perceptron learning algorithm
Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Single Layer Perceptron Learning (SLPL) algorithm has a very low computational requirement which makes it suitable for online BCI system...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English English |
| 出版事項: |
2014
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| 主題: | |
| オンライン･アクセス: | http://irep.iium.edu.my/39001/1/39001_edited.pdf http://irep.iium.edu.my/39001/4/39001_Classification%20of%20multichannel%20EEG%20signal_Scopus.pdf http://irep.iium.edu.my/39001/ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7031650 |
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| 要約: | Motor imagery (MI) related Electroencephalogram
(EEG) signal classification is one of the main challenge in
designing a brain computer interface (BCI) system. Single Layer
Perceptron Learning (SLPL) algorithm has a very low
computational requirement which makes it suitable for online
BCI system. This paper proposes an advanced and simple
classification technique for MI related BCI system. Initially the
signal is extracted for different features. The SLPL classifier has
been used to propose technique to design an MI based BCI. For
contrastive comparison with other classification techniques have
been evaluated by classification accuracy, mutual information
and Cohen’s kappa. |
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