Regularization using geometric information between sensors capturing features from brain signals

We propose a regularization based on geometric structure for feature extraction in a sensor array for brain data recordings. The purpose of the study is to add a penalty term using distances between sensors as the geometric information for finding spatial weights. The regularization term is derived under the definition of neighbors of sensors. We evaluate the proposed regularization in common spatial pattern (CSP) which is a well-known feature extraction method for EEG based brain computer interface (BCI). We have demonstrated the CSP procedure with the regularization by simulation for artificial signals. The results show that the proposed method works better than standard CSP in extracting of a component generated in a certain brain spot. Moreover, the classification experimental results using dataset of motor imagery based BCI suggest that the proposed method achieved maximum improvement by 27% in the classification accuracy over the standard CSP in a setting of even when we use only five samples.