Paper Info
Title
Subject-to-subject transfer for CSP based BCIs: feature space transformation and decision-level fusion.
Abstract
Modern Brain Computer Interfaces (BCIs) usually require a calibration session to train a machine learning system before each usage. In general, such trained systems are highly specialized to the subject's characteristic activation patterns and cannot be used for other sessions or subjects. This paper presents a feature space transformation that transforms features generated using subject-specific spatial filters into a subject-independent feature space. The transformation can be estimated from little adaptation data of the subject. Furthermore, we combine three different Common Spatial Pattern based feature extraction approaches using decision-level fusion, which enables BCI use when little calibration data is available, but also outperformed the subject-dependent reference approaches for larger amounts of training data.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6610823
EMBC
Keywords
Field
DocType
subject-independent feature space,subject-to-subject transfer,calibration,subject-specific spatial filters,spatial filters,learning (artificial intelligence),brain-computer interfaces,feature extraction,common spatial patterns,decision-level fusion,machine learning system,feature space transformation,sensor fusion,brain computer interfaces,electroencephalography,learning artificial intelligence,testing
Decision level,Computer science,Brain–computer interface,Fusion,Artificial intelligence,Training set,Computer vision,Feature vector,Pattern recognition,Feature (computer vision),Sensor fusion,Feature extraction,Machine learning
Conference
Volume
ISSN
Citations 
2013
1557-170X
2
PageRank 
References 
Authors
0.40
6
4
Name
Order
Citations
PageRank
Dominic Heger1508.29
Felix Putze220529.73
Christian Herff3297.25
T. Schultz42423252.72