In this paper, we introduce a novel linear discriminate analysis (LDA) ensemble classifier utilizing the Mindo as a brain-computer interface (BCI) device to deal with the problem of motor imagery classification. With regard to the composition of the proposed system, we combine filter bank, sub-band common spatial pattern (SBCSP), LDA together for extracting features of EEG data and classifying the motor imagery with left or right states. In addition, we also employ a gradient descent (GD) algorithm to find the best weight associated with probability fusion function. This novel architecture not only boosts the accuracy of classification but maintains the computational efficiency of the system. Therefore, the proposed LDA-ensemble framework is able to be satisfied with each subject as demonstrated in Section III.
|Title of host publication||INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014)|
|Number of pages||7|
|ISBN (Print)||978-1-61499-484-8, 978-1-61499-483-1|
|State||Published - 2015|
- brain-computer interface (BCI)
- motor imagery
- linear discriminate analysis (LDA)
Chiu, C-Y., Chen, C-Y., Lin, Y-Y., Chen, S-A., & Lin, C-T. (2015). Using a Novel LDA-Ensemble Framework to Classification of Motor Imagery Tasks for Brain-Computer Interface Applications. In INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014) (Vol. 274, pp. 150-156). IOS Press.