A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface
2013
IEEE Transactions on Neural Networks and Learning Systems
A major challenge in EEG-based brain-computer interfaces (BCIs) is the inherent non-stationarities in the EEG data. Variations of the signal properties from intra and inter sessions often lead to deteriorated BCI performances as features extracted by methods such as common spatial patterns (CSP) are not invariant against the changes. To extract features that are robust and invariant, this paper proposes a novel spatial filtering algorithm, called KLCSP. The CSP algorithm only considers the
doi:10.1109/tnnls.2013.2239310
pmid:24808381
fatcat:egwswtp7prbn3dgzsj7namsqrm