TY - GEN
T1 - Tackling noise, artifacts and nonstationarity in BCI with robust divergences
AU - Samek, Wojciech
AU - Muller, Klaus Robert
N1 - Funding Information:
This work was supported by the by the Federal Ministry of Education and Research (BMBF) under the project Adaptive BCI (FKZ 01GQ1115) and by the Brain Korea 21 Plus Program through the National Research Foundation of Korea funded by the Ministry of Education
Publisher Copyright:
© 2015 EURASIP.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - Although the field of Brain-Computer Interfacing (BCI) has made incredible advances in the last decade, current BCIs are still scarcely used outside laboratories. One reason is the lack of robustness to noise, artifacts and nonstationarity which are intrinsic parts of the recorded brain signal. Furthermore out-of-lab environments imply the presence of external variables that are largely beyond the control of the user, but can severely corrupt signal quality. This paper presents a new generation of robust EEG signal processing approaches based on the information geometric notion of divergence. We show that these divergence-based methods can be used for robust spatial filtering and thus increase the systems' reliability when confronted to, e.g., environmental noise, users' motions or electrode artifacts. Furthermore we extend the divergence-based framework to heavy-tail distributions and investigate the advantages of a joint optimization for robustness and stationarity.
AB - Although the field of Brain-Computer Interfacing (BCI) has made incredible advances in the last decade, current BCIs are still scarcely used outside laboratories. One reason is the lack of robustness to noise, artifacts and nonstationarity which are intrinsic parts of the recorded brain signal. Furthermore out-of-lab environments imply the presence of external variables that are largely beyond the control of the user, but can severely corrupt signal quality. This paper presents a new generation of robust EEG signal processing approaches based on the information geometric notion of divergence. We show that these divergence-based methods can be used for robust spatial filtering and thus increase the systems' reliability when confronted to, e.g., environmental noise, users' motions or electrode artifacts. Furthermore we extend the divergence-based framework to heavy-tail distributions and investigate the advantages of a joint optimization for robustness and stationarity.
KW - Brain-Computer Interfacing
KW - Common Spatial Patterns
KW - Nonstationarity
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=84963939269&partnerID=8YFLogxK
U2 - 10.1109/EUSIPCO.2015.7362883
DO - 10.1109/EUSIPCO.2015.7362883
M3 - Conference contribution
AN - SCOPUS:84963939269
T3 - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
SP - 2741
EP - 2745
BT - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd European Signal Processing Conference, EUSIPCO 2015
Y2 - 31 August 2015 through 4 September 2015
ER -