TY - JOUR
T1 - Enhanced active segment selection for single-trial EEG classification
AU - Hsu, Wei Yen
PY - 2012/4
Y1 - 2012/4
N2 - In this study, an electroencephalogram (EEG) analysis system is proposed for single-trial classification of both motor imagery (MI) and finger-lifting EEG data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of three procedures; enhanced active segment selection, feature extraction, and classification. In addition to the original use of continuous wavelet transform (CWT) and Student 2-sample t statistics, the two-dimensional (2D) anisotropic Gaussian filter further refines the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by using proposed modified fractal dimension. Finally, the support vector machine (SVM) is used for classification. Compared to original active segment selection, with several popular features and classifier on both the MI and finger-lifting data from 2 data sets, the results indicate that the proposed method is promising in EEG classification.
AB - In this study, an electroencephalogram (EEG) analysis system is proposed for single-trial classification of both motor imagery (MI) and finger-lifting EEG data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of three procedures; enhanced active segment selection, feature extraction, and classification. In addition to the original use of continuous wavelet transform (CWT) and Student 2-sample t statistics, the two-dimensional (2D) anisotropic Gaussian filter further refines the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by using proposed modified fractal dimension. Finally, the support vector machine (SVM) is used for classification. Compared to original active segment selection, with several popular features and classifier on both the MI and finger-lifting data from 2 data sets, the results indicate that the proposed method is promising in EEG classification.
KW - active segment selection
KW - brain-computer interface (BCI)
KW - electroencephalogram (EEG)
KW - modified fractal dimension
KW - support vector machine (SVM)
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84865769776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865769776&partnerID=8YFLogxK
U2 - 10.1177/1550059412445051
DO - 10.1177/1550059412445051
M3 - Article
C2 - 22715494
AN - SCOPUS:84865769776
VL - 43
SP - 87
EP - 96
JO - Clinical EEG and Neuroscience
JF - Clinical EEG and Neuroscience
SN - 1550-0594
IS - 2
ER -