In motor-imagery brain–computer interface (BCI), transfer learning based on the framework of regularized common spatial patterns (RCSP) can make full use of the training data derived from other subjects to reduce calibration time for a new subject. Covariance matrices are commonly used to estimate the difference between subjects. However, the classification performances vary greatly depending on different assumptions of the distribution of covariance matrices. Therefore, to directly observe the variations of the target subject’s features after transferring, we neglect the distribution of covariance matrices and instead compare cosine similarities of spatial filters between the target subject and the composite subject whose data comes from the target subject and a source subject. Two RCSP algorithms based on cosine measure are proposed to use the samples of all source subjects and most useful source subjects, respectively. Experiments on one public data set from BCI competition show that our proposed approaches significantly improve the classification performances compared to the conventional CSP algorithm in almost every case, based on a small training set.
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns