Title :
Incremental Common Spatial Pattern algorithm for BCI
Author :
Zhao, Qibin ; Zhang, Liqing ; Cichocki, Andrzej ; Li, Jie
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai
Abstract :
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm. To overcome this, in this paper, we propose the Incremental Common Spatial Pattern (ICSP) algorithm which performs the adaptive feature extraction on-line. This method allows us to perform the online adjustment of spatial filter. This procedure helps the BCI system robust to possible non-stationarity of the EEG data. We test our method to data from BCI motor imagery experiments, and the results demonstrate the good performance of adaptation of the proposed algorithm.
Keywords :
brain-computer interfaces; electroencephalography; feature extraction; learning (artificial intelligence); pattern classification; EEG data; adaptive feature extraction; batch type algorithm; brain signals; brain-computer interfaces; incremental common spatial pattern algorithm; machine learning methods; spatial filter online adjustment; Brain computer interfaces; Communication system control; Covariance matrix; Data mining; Electroencephalography; Feature extraction; Robustness; Spatial filters; Testing; Training data;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634170
