@INPROCEEDINGS{6610823,
author={Heger, D. and Putze, F. and Herff, C. and Schultz, T.},
booktitle={Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE},
title={Subject-to-subject transfer for CSP based BCIs: Feature space transformation and decision-level fusion},
year={2013},
pages={5614-5617},
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.},
keywords={brain-computer interfaces;calibration;feature extraction;learning (artificial intelligence);sensor fusion;spatial filters;brain computer interfaces;calibration;common spatial patterns;decision-level fusion;feature extraction;feature space transformation;machine learning system;subject-independent feature space;subject-specific spatial filters;subject-to-subject transfer;Brain-computer interfaces;Calibration;Electroencephalography;Feature extraction;Testing;Training;Transforms},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/EMBC13_2191_FI.pdf},
doi={10.1109/EMBC.2013.6610823},
ISSN={1557-170X},
month={July},}