TY - GEN
T1 - Improving classification performance of BCIs by using stationary common spatial patterns and unsupervised bias adaptation
AU - Wojcikiewicz, Wojciech
AU - Vidaurre, Carmen
AU - Kawanabe, Motoaki
PY - 2011
Y1 - 2011
N2 - Non-stationarities in EEG signals coming from electrode artefacts, muscular activity or changes of task involvement can negatively affect the classification accuracy of Brain-Computer Interface (BCI) systems. In this paper we investigate three methods to alleviate this: (1) Regularization of Common Spatial Patterns (CSP) towards stationary subspaces in order to reduce the influence of artefacts. (2) Unsupervised adaptation of the classifier bias with the goal to account for systematic shifts of the features occurring for example in the transition from calibration to feedback session or with increasing fatigue of the subject. (3) Decomposition of the CSP projection matrix into a whitening and a rotation part and adaptation of the whitening matrix in order to reduce the influence of non-task related changes. We study all three approaches on a data set of 80 subjects and show that stationary features with bias adaptation significantly outperforms the other combinations.
AB - Non-stationarities in EEG signals coming from electrode artefacts, muscular activity or changes of task involvement can negatively affect the classification accuracy of Brain-Computer Interface (BCI) systems. In this paper we investigate three methods to alleviate this: (1) Regularization of Common Spatial Patterns (CSP) towards stationary subspaces in order to reduce the influence of artefacts. (2) Unsupervised adaptation of the classifier bias with the goal to account for systematic shifts of the features occurring for example in the transition from calibration to feedback session or with increasing fatigue of the subject. (3) Decomposition of the CSP projection matrix into a whitening and a rotation part and adaptation of the whitening matrix in order to reduce the influence of non-task related changes. We study all three approaches on a data set of 80 subjects and show that stationary features with bias adaptation significantly outperforms the other combinations.
KW - Brain-Computer Interface
KW - Common Spatial Patterns
KW - adaptive classification
KW - stationary features
UR - https://www.scopus.com/pages/publications/79957918545
U2 - 10.1007/978-3-642-21222-2_5
DO - 10.1007/978-3-642-21222-2_5
M3 - Conference contribution
AN - SCOPUS:79957918545
SN - 9783642212215
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 34
EP - 41
BT - Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings
T2 - 6th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2011
Y2 - 23 May 2011 through 25 May 2011
ER -