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Yuan Shi
Dalian Institute of Science and Technology

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Study on Mahalanobis Discriminant Analysis of EEG Data Yuan Shi; Linlin Yu; Fang Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5387-5391

Abstract

Objective In this paper, we have done Mahalanobis Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. Methods In accordance with the strength of wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Mahalanobis Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Mahalanobis Discriminant analysis, the electrode classification accuracy rates is 64.4%. Conclusions Mahalanobis Discriminant has higher prediction accuracy, EEG features (mainlywave) extract more accurate. Mahalanobis Discriminant would be better applied to the feature extraction and classification decisions of EEG data.
Study on Fisher Analysis of Electroencephalograph Data Yuan Shi; Qi Wei; Ruijie Liu; Yuli Ge
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 5: May 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Objective In this paper, we have done Fisher discriminant analysis to Electroencephalogram (EEG) data of experiment objects which are recorded impersonally, come up with a relatively accurate method used in feature extraction and classification decisions. The present study is the groundwork analysis for other analysis in EEG study. Methods In accordance with the strength of  wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Fisher discriminant analysis to EEG data of six objects. EEG data processing and statistic analysis adopted independently designed EEG analysis toolbox and the program of correlation analysis. Results In use of part of EEG data of 63 people, we have done Fisher discriminant analysis, the electrode classification accuracy rates is 82.3%. Conclusions Fisher discriminant has higher prediction accuracy, EEG features (mainly  wave) extract more accurate. Fisher discriminant would be better applied to the feature extraction and classification decisions of EEG data. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2464
Co-Authors Fang Qin Linlin Yu Qi Wei Ruijie Liu Yuli Ge