Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern.
10.7507/1001-5515.202302020
- Author:
Keling FEI
1
;
Xiaoxian CAI
1
;
Shunzhi CHEN
1
;
Lizheng PAN
1
;
Wei WANG
2
Author Information
1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China.
2. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, P.R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Common spatial pattern;
Electroencephalogram signal recognition;
Feature selection;
Flicker noise spectroscopy
- MeSH:
Brain-Computer Interfaces;
Imagination;
Signal Processing, Computer-Assisted;
Electroencephalography/methods*;
Algorithms;
Spectrum Analysis
- From:
Journal of Biomedical Engineering
2023;40(6):1126-1134
- CountryChina
- Language:Chinese
-
Abstract:
Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
