Spatial Filter Selection with LASSO for EEG Classification

摘要：

Spatial filtering is an important step of preprocessing for electroencephalogram (EEG) signals. Extreme energy ratio (EER) is a recently proposed method to learn spatial niters for EEG classification. It selects several eigenvectors from top and end of the eigenvalue spectrum resulting from a spectral decomposition to construct a group of spatial niters as a filter bank. However, that strategy has some limitations and the spatial filters in the group are often selected improperly. Therefore the energy features filtered by the filter bank do not contain enough discriminative information or severely overfit on small training samples. This paper utilize one of the penalized feature selection strategies called LASSO to aid us to construct the spatial filter bank termed LASSO spatial filter bank. It can learn a better selection of the spatial filters. Then two different classification methods are presented to evaluate our LASSO spatial filter bank. Their excellent performances demonstrate the stronger generalization ability of the LASSO spatial filter bank, as shown by the experimental results.

关键词： Brain-computer interface Common spatial patterns Extreme energy ratio Feature extraction Feature selectionLASSO

作者: Wenting Tu Shiliang Sun

作者单位: Department of Computer Science and Technology, East China Normal University 500 Dongchuan Road, Shanghai 200241 P.R. China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 142-149

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）