High Resolution Common Spatial Frequency Filters for Classifying Multi-class EEG
The Common Spatial Patterns (CSP) algorithm is a highly successful spatial filtering method for extracting spatial patterns related to specific mental tasks from electroencephalogram (EEG) signals. The performance of CSP highly depends on the selection of frequency band in the preprocess. However, the most discriminative frequency band features varies slightly with subjects and mental tasks. In order to provide high resolution in frequency domain, we propose an common spatial frequency patterns method to learn most discriminative spatial and frequency filters simultaneously for specific mental task. The results on EEG data during motor imagery (MI) tasks demonstrate the good performance of our method with decreased number of EEG channels.
EEG BCI CSP
Qibin Zhao Tomasz M. Rutkowski Andrzej Cichocki Liqing Zhang
Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama,Japan
国际会议
The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)
杭州
英文
683-688
2009-11-15（万方平台首次上网日期，不代表论文的发表时间）