Study on Mahalanobis Discriminant Analysis of EEG Data

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.