TY - JOUR
T1 - Stationary common spatial patterns for brain-computer interfacing
AU - Samek, Wojciech
AU - Vidaurre, Carmen
AU - Müller, Klaus Robert
AU - Kawanabe, Motoaki
PY - 2012/4
Y1 - 2012/4
N2 - Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.
AB - Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.
UR - http://www.scopus.com/inward/record.url?scp=84857884326&partnerID=8YFLogxK
U2 - 10.1088/1741-2560/9/2/026013
DO - 10.1088/1741-2560/9/2/026013
M3 - Article
C2 - 22350439
AN - SCOPUS:84857884326
SN - 1741-2560
VL - 9
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 2
M1 - 026013
ER -