Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis.
- Author:
Jinzhi ZHOU
;
Xiaofang TANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Discriminant Analysis;
Electroencephalography;
Fourier Analysis;
Humans;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2015;32(4):735-739
- CountryChina
- Language:Chinese
-
Abstract:
In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.
