Sidath R. Liyanage, Jialin S. Pan, Haihong Zhang, Kai Keng Ang, Cuntai Guan, Jian-Xin Xu, and Tong H. Lee
Brain-computer interfaces , Transfer Learning, Transfer Component Analysis, Electroencephalography
Motion intention can be detected from human Electroencephalography (EEG) signals through BCI, which can facilitate motor motion control for disabled or paralyzed people. However, the continuous use of BCI is hindered by the non-stationarity of the EEG signals. This paper proposes a method to identify the EEG signal components that can be used to train a classifier to address the non-stationarity issue. The proposed method is based on Transfer Component Analysis (TCA). TCA seeks to locate components that can be transferred across domains in a Reproducing Kernel Hilbert Space (RKHS). The distributions associated with data are closer to each other in the subspaces spanned by the identified transfer components. Therefore, typical machine learning techniques can be applied in the subspace spanned by these transfer components. This results in classifiers that can be trained on the source domain and tested on the target domain. The proposed Stationary Transfer Component Analysis (STCA) method is compared with Stationary Sub-space Analysis (SSA) on the BCI competition IV dataset 2a. The results show significant improvements over the baseline case and the results are better than those produced by SSA.
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