Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

@article{Azab2019DynamicTW,
  title={Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.},
  author={Ahmed M. Azab and Hamed Ahmadi and L. Mihaylova and M. Arvaneh},
  journal={Journal of neural engineering},
  year={2019}
}
OBJECTIVE Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. APPROACH… Expand
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