Abstract
In the context of brain-computer interface (BCI) system, the common spatial patterns (CSP) method has been used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals. However, the classification performance of CSP typically deteriorates when a few training samples are collected from a new BCI user. In this paper, we propose an approach that maintains or improves the recognition accuracy of the system with only a small size of training data set. The proposed approach is formulated by regularizing the classical CSP technique with the strategy of transfer learning. Specifically, we incorporate into the CSP analysis inter-subject information involving the same task, by minimizing the difference between the inter-subject features. Experimental results on two data sets from BCI competitions show that the proposed approach greatly improves the classification performance over that of the conventional CSP method; the transformed variant proved to be successful in almost every case, based on a small number of available training samples.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their thoughtful comments and suggestions. This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704 and the National Natural Science Foundation of China under Grant 61375118.
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Cheng, M., Lu, Z. & Wang, H. Regularized common spatial patterns with subject-to-subject transfer of EEG signals. Cogn Neurodyn 11, 173–181 (2017). https://doi.org/10.1007/s11571-016-9417-x
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DOI: https://doi.org/10.1007/s11571-016-9417-x