Feature extraction and classification of EEG for mental tasks based on wavelet packet analysis / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 397-400, 2004.
Article
in Zh
| WPRIM
| ID: wpr-291103
Responsible library:
WPRO
ABSTRACT
This paper explores the use of wavelet packet analysis to extract features from spontaneous electroencephalogram (EEG) during three different mental tasks. Artifact-free EEG segments are transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Their feature vectors formed by energy values of different sub-spaces EEG components are used as inputs of a radial basis function network to test the classification accuracies of three task pairs. The results indicate that the classification accuracies of the wavelet packet analysis method are significantly better than those of autoregressive model method. Wavelet packet analysis would be a promising method to extract features from EEG signals.
Full text:
1
Index:
WPRIM
Main subject:
Physiology
/
Signal Processing, Computer-Assisted
/
Multivariate Analysis
/
Regression Analysis
/
Models, Statistical
/
Neural Networks, Computer
/
Electroencephalography
/
Mental Processes
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
Zh
Journal:
Journal of Biomedical Engineering
Year:
2004
Type:
Article