The implementation of two signature-based methods applied on EEG-based brain-computer interfaces (BCIs). The first method uses the path signature directly as a feature vector. The second method takes negative square of the lead matrix constructed from the second level signature and adds a regularization term to get a symmetric positive definite (SPD) matrix to be used as features.
A simple example of classification of left/right motor imagery of one subject in Physionet MI dataset is given. However these methods are general and can be applied to other paradigms of EEG-based BCIs.
- Numpy
- Scikit-learn
- MNE
- PyRiemann
- PyTorch
- Signatory
Xu, X., Lee, D., Drougard, N. et al. Signature methods for brain-computer interfaces. Sci Rep 13, 21367 (2023). https://doi.org/10.1038/s41598-023-41326-8
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