TY - GEN
T1 - Brain-computer interfacing in discriminative and stationary subspaces
AU - Samek, Wojciech
AU - Muller, Klaus Robert
AU - Kawanabe, Motoaki
AU - Vidaurre, Carmen
PY - 2012
Y1 - 2012
N2 - The non-stationary nature of neurophysiological measurements, e.g. EEG, makes classification of motion intentions a demanding task. Variations in the underlying brain processes often lead to significant and unexpected changes in the feature distribution resulting in decreased classification accuracy in Brain Computer Interfacing (BCI). Several methods were developed to tackle this problem by either adapting to these changes or extracting features that are invariant. Recently, a method called Stationary Subspace Analysis (SSA) was proposed and applied to BCI data. It diminishes the influence of non-stationary changes as learning and classification is performed in a stationary subspace of the data which can be extracted by SSA. In this paper we extend this method in two ways. First we propose a variant of SSA that allows to extract stationary subspaces from labeled data without disregarding class-related variations or treating class-differences as non-stationarities. Second we propose a discriminant variant of SSA that trades-off stationarity and discriminativity, thus it allows to extract stationary subspaces without losing relevant information. We show that learning in a discriminative and stationary subspace is advantageous for BCI application and outperforms the standard SSA method.
AB - The non-stationary nature of neurophysiological measurements, e.g. EEG, makes classification of motion intentions a demanding task. Variations in the underlying brain processes often lead to significant and unexpected changes in the feature distribution resulting in decreased classification accuracy in Brain Computer Interfacing (BCI). Several methods were developed to tackle this problem by either adapting to these changes or extracting features that are invariant. Recently, a method called Stationary Subspace Analysis (SSA) was proposed and applied to BCI data. It diminishes the influence of non-stationary changes as learning and classification is performed in a stationary subspace of the data which can be extracted by SSA. In this paper we extend this method in two ways. First we propose a variant of SSA that allows to extract stationary subspaces from labeled data without disregarding class-related variations or treating class-differences as non-stationarities. Second we propose a discriminant variant of SSA that trades-off stationarity and discriminativity, thus it allows to extract stationary subspaces without losing relevant information. We show that learning in a discriminative and stationary subspace is advantageous for BCI application and outperforms the standard SSA method.
UR - http://www.scopus.com/inward/record.url?scp=84882949036&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6346563
DO - 10.1109/EMBC.2012.6346563
M3 - Conference contribution
C2 - 23366524
AN - SCOPUS:84882949036
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2873
EP - 2876
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -