Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis.
10.7507/1001-5515.201809019
- Author:
Rongrong FU
1
;
Yongsheng TIAN
2
;
Tiantian BAO
2
Author Information
1. College of Electrical Engineering, Qinhuangdao, Hebei 066004, P.R.China;Key Lab of Measurement Technology & Instrumentation of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.frr1102@aliyun.com.
2. College of Electrical Engineering, Qinhuangdao, Hebei 066004, P.R.China;Key Lab of Measurement Technology & Instrumentation of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.
- Publication Type:Journal Article
- Keywords:
Fisher linear discriminant analysis;
brain-computer interface;
common spatial pattern;
motor imagery electroencephalogram;
sparse
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Discriminant Analysis;
Electroencephalography;
Imagination;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2019;36(6):911-915
- CountryChina
- Language:Chinese
-
Abstract:
This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.
