Laplacian filters are widely used in neuroscience. In the context of brain-computer interfacing, they might be preferred to data-driven approaches such as common spatial patterns (CSP) in a variety of scenarios such as, e.g., when no or few user data are available or a calibration session with a multi-channel recording is not possible, which is the case in various applications. In this paper we propose the use of an ensemble of local CSP patches (CSPP) which can be considered as a compromise between Laplacian filters and CSP. Our CSPP only needs a very small number of trials to be optimized and significantly outperforms Laplacian filters in all settings studied. Additionally, CSPP also outperforms multi-channel CSP and a regularized version of CSP even when only very few calibration data are available, acting as a CSP regularizer without the need of additional hyperparameters and at a very low cost: 2-5 min of data recording, i.e. ten times less than CSP.
ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience