Lin, C.T., Hsieh, T.Y., Liu, Y.T., Lin, Y.Y., Fang, C.N., Wang, Y.K., Yen, G., Pal, N.R. & Chuang, C.H. 2018, 'Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 950-962.View/Download from: UTS OPUS or Publisher's site
© 1989-2012 I EEE. The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
Hsieh, T.Y., Lin, Y.Y., Liu, Y.T., Fang, C.N. & Lin, C.T. 2015, 'Developing a novel multi-fusion brain-computer interface (BCI) system with particle swarm optimization for motor imagery task', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey.View/Download from: UTS OPUS or Publisher's site
© 2015 IEEE. In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.
Lin, C.-.T., Wang, Y.-.K., Fang, C.-.N., Yu, Y.-.H. & King, J.-.T. 2015, 'Extracting patterns of single-trial EEG using an adaptive learning algorithm.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Milan, ITALY, pp. 6642-6645.View/Download from: Publisher's site
The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.