Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

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 tim...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience Vol. 2011; no. 2011; pp. 63 - 71
Main Authors: Otte, Georges, Wyns, Bart, Grosse-Wentrup, Moritz, Santens, Patrick, Devlaminck, Dieter
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Limiteds 01.01.2011
Hindawi Puplishing Corporation
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial Intelligence
Humans
Imagination - physiology
Machine learning
Motor Activity - physiology
User interface
User-Computer Interface
Belgium
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary: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.
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Academic Editor: Fabio Babiloni
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2011/217987