Compensating changes between a subjects' training and testing session in brain-computer interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, and thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common nonstationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multi-subject methods that, e.g., improve the covariance matrix estimation by shrinking it toward the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper, we compare our approach to two state-of-the-art multi-subject methods on toy data and two datasets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.
The method proposed by Lotte and Guan @cite_21 regularizes the estimated covariance matrix towards the average covariance matrix of other subjects. This kind of regularization may largely improve the estimation quality of the high dimensional covariance matrix if data is scarce. The estimation for subject @math can be written as where @math is the covariance matrix of class @math for the subject of interest, @math are the covariance matrices of the other @math subjects and @math is a regularization parameter controlling the amount of information incorporated from other users. This method is based on a very restrictive assumption, namely the similarity between covariance matrices of different subjects. The authors in @cite_21 recognized that this assumption is often violated due to large inter-subject variability, thus they proposed a sequential algorithm for subject selection. In the following we will refer to this approach as covariance-based CSP (covCSP).
@cite_21
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A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.