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Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.
A novel regularized covariance matrix estimation framework for CSP based on dynamic time warping (DTW) and transfer learning and significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Expand
Robust Common Spatial Patterns Estimation Using Dynamic Time Warping to Improve BCI Systems
A novel dynamic time warping (DTW)-based approach to improve CSP covariance matrix estimation and hence improve feature extraction and the results showed that the proposed approach improved the classification accuracy from 78% to 83% on average. Expand