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A Method of Feature Extraction on Motor Imagery EEG Using FLD and PCA Based on Sub-Band CSP
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 12,  2015, pp.1535-1543
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.12.1535
 Title & Authors
A Method of Feature Extraction on Motor Imagery EEG Using FLD and PCA Based on Sub-Band CSP
Park, Sang-Hoon; Lee, Sang-Goog;
 
 Abstract
The brain-computer interface obtains a user's electroencephalogram as a replacement communication unit for the disabled such that the user is able to control machines by simply thinking instead of using hands or feet. In this paper, we propose a feature extraction method based on a non-selected filter by SBCSP to classify motor imagery EEG. First, we divide frequencies (4~40 Hz) into 4-Hz units and apply CSP to each Unit. Second, we obtain the FLD score vector by combining FLD results. Finally, the FLD score vector is projected onto the optimal plane for classification using PCA. We use BCI Competition III dataset IVa, and Extracted features are used as input for LS-SVM. The classification accuracy of the proposed method was evaluated using fold cross-validation. For subjects 'aa', 'al', 'av', 'aw', and 'ay', results were , , , , and , respectively.
 Keywords
Brain-Computer Interface;Electroencephalogram;Motor Imagery;CSP;FLD;Feature Extraction;PCA;Classification;
 Language
Korean
 Cited by
1.
Small Sample Setting and Frequency Band Selection Problem Solving Using Subband Regularized Common Spatial Pattern, IEEE Sensors Journal, 2017, 17, 10, 2977  crossref(new windwow)
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