Joint optimization for discriminative, compact and robust Brain-Computer Interfacingby D. Heger, C. Herff, F. Putze, T. Schultz
Abstract:
We present a new pattern recognition framework for Brain-Computer Interfacing that learns discriminative brain activity patterns, compact modeling, and robustness against signal variabilities by a single joint optimization. We present an algorithm based on the Alternating Direction Method of Multipliers, which finds an optimal solution for this approach extremely efficiently. A first evaluation using a publicly available EEG motor imagery data corpus with 105 subjects shows that our framework outperformed state-of-the-art methods and successfully performed subject transfer.
Reference:
Joint optimization for discriminative, compact and robust Brain-Computer Interfacing (D. Heger, C. Herff, F. Putze, T. Schultz), In Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on, 2015.
Bibtex Entry:
@INPROCEEDINGS{7146565,
author={Heger, D. and Herff, C. and Putze, F. and Schultz, T.},
booktitle={Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on},
title={Joint optimization for discriminative, compact and robust Brain-Computer Interfacing},
year={2015},
pages={82-85},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/ner_2015_paper_heger.pdf},
abstract={We present a new pattern recognition framework for Brain-Computer Interfacing that learns discriminative brain activity patterns, compact modeling, and robustness against signal variabilities by a single joint optimization. We present an algorithm based on the Alternating Direction Method of Multipliers, which finds an optimal solution for this approach extremely efficiently. A first evaluation using a publicly available EEG motor imagery data corpus with 105 subjects shows that our framework outperformed state-of-the-art methods and successfully performed subject transfer.},
keywords={brain-computer interfaces;electroencephalography;medical signal processing;neurophysiology;optimisation;pattern recognition;EEG motor imagery data corpus;alternating direction method of multipliers;compact modeling;discriminative brain activity patterns;pattern recognition framework;robust brain-computer interfacing;single joint optimization;Brain;Electroencephalography;Feature extraction;Optimization;Pattern recognition;Robustness;Training},
doi={10.1109/NER.2015.7146565},
month={April},}