Study on Fisher Analysis of Electroencephalograph Data

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