Classification of multichannel EEG signal by linear discriminant analysis
Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Linear Discriminant Analysis (LDA) has a very low computational requirement which makes it suitable for online BCI system. This paper propose...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English English |
| Published: |
Springer International Publishing AG
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38995/1/279.pdf http://irep.iium.edu.my/38995/4/ICSEng_2014_-_23rd_INTERNATIONAL_CONFERENCE_ON_SYSTEMS_ENGINEERING.pdf http://irep.iium.edu.my/38995/7/42469_Classification%20of%20multichannel%20EEG%20signal%20by%20linear_Scopus.pdf http://irep.iium.edu.my/38995/ http://link.springer.com/chapter/10.1007%2F978-3-319-08422-0_42#page-1 |
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| Summary: | Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Linear Discriminant Analysis (LDA) 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 LDA classifier has been used to propose technique to design an MI based BCI. For contrastive comparison other classification techniques have been evaluated by classification accuracy and Cohen's kappa. |
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