TY - JOUR
T1 - Investigation of methods for extracting features related to motor imagery and resting states in EEG-based BCI system
AU - Susila, I. Putu
AU - Kanoh, Shin'Ichiro
AU - Miyamoto, Ko Ichiro
AU - Yoshinobu, Tatsuo
PY - 2009
Y1 - 2009
N2 - Methods for extracting features of motor imagery from 1-channel bipolar EEG were evaluated. The EEG power spectrums which were used as feature vectors were calculated with filter bank, FFT and AR model, and were then classified by linear discriminant analysis (LDA) to discriminate motor imagery and resting states. It was shown that the extraction method using AR model gave the best result with the average true positive rate of 83% (a -7%). Furthermore, when principal component analysis (PCA) was applied to the feature vectors, the dimension of the feature vectors could be reduced without decreasing accuracy of discrimination.
AB - Methods for extracting features of motor imagery from 1-channel bipolar EEG were evaluated. The EEG power spectrums which were used as feature vectors were calculated with filter bank, FFT and AR model, and were then classified by linear discriminant analysis (LDA) to discriminate motor imagery and resting states. It was shown that the extraction method using AR model gave the best result with the average true positive rate of 83% (a -7%). Furthermore, when principal component analysis (PCA) was applied to the feature vectors, the dimension of the feature vectors could be reduced without decreasing accuracy of discrimination.
KW - Brain-computer interface (BCI)
KW - Feature extraction
KW - Linear discriminant analysis (LDA), principal component analysis (PCA)
KW - Motor imagery
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U2 - 10.1541/ieejeiss.129.1828
DO - 10.1541/ieejeiss.129.1828
M3 - Article
AN - SCOPUS:70450219417
SN - 0385-4221
VL - 129
SP - 1828
EP - 1833
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
IS - 10
ER -