Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI
By D. Devlaminck, B. Wyns, Moritz Grosse-Wentrup, G. Otte, P. Santens. Computational Intelligence and Neuroscience, 2011
524 citations recorded by the source corpus.
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
Subfields: Cognitive Neuroscience, Human-Computer Interaction, Signal Processing
View Paper: https://www.semanticscholar.org/paper/13d8d4b8515717fba3b7dbe6cbafa64511140230
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Human-Computer Interaction
In Signal Processing
- Moritz Grosse-Wentrup
- Bernhard Schölkopf
- Yasemin Altun
- Morteza Alamgir
- Arthur Gretton
- Dominik Janzing
- Kenji Fukumizu
- David Lopez-Paz
Citations in the corpus
- Transfer Learning in Brain-Computer Interfaces (2015, background): “In these contributions, the authors demonstrate successful subject-to-subject transfer by regularizing spatial filters derived by CSP with data from other subjects, which amounts to attempting to find an invariant subspace on which to project the data of new subjects.”