title: Study on bayes discriminant analysis of EEG data.
abstract: OBJECTIVE
  In this paper, we have done Bayes 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 α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%.
  
  
  CONCLUSIONS
  Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.
authors: School of Soft Engineering, Dalian Institute of Science and Technology, Dalian, Liaoning, 116028, P.R. China.
authors: Yuan
authors: Y
authors: Shi
authors: School of Soft Engineering, Dalian Institute of Science and Technology, Dalian, Liaoning, 116028, P.R. China.
authors: DanDan
authors: D
authors: He
authors: School of Soft Engineering, Dalian Institute of Science and Technology, Dalian, Liaoning, 116028, P.R. China.
authors: Fang
authors: F
authors: Qin
keywords: Bayes Discriminant; Electroencephalogram; α rhythm