RESEARCH ARTICLE
Study on Bayes Discriminant Analysis of EEG Data
Yuan Shi*, DanDan He, Fang Qin
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 142
Last Page: 146
Publisher ID: TOBEJ-8-142
DOI: 10.2174/1874120701408010142
Article History:
Received Date: 22/09/2014Revision Received Date: 30/11/2014
Acceptance Date: 02/12/2014
Electronic publication date: 31/12/2014
Collection year: 2014
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
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 α wave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.