期刊
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
卷 -, 期 -, 页码 1-7出版社
SPRINGER
DOI: 10.1186/1687-6180-2012-129
关键词
BCI; Non-stationarity; Covariate shift adaption; PCA
资金
- German Federal Ministry of Education and Research (BMBF, BFNT F*T) [UTu 01 GQ 0831]
- DFG [RO 1030/15-1]
One of the biggest problems in today's BCI research is the non-stationarity of the recorded signals. This non-stationarity can cause the BCI performance to deteriorate over time or drop significantly when transferring data from one session to another. To reduce the effect of non-stationaries, we propose a new method for covariate shift adaption that is based on Principal Component Analysis to extract non-stationaries and alleviate them. We show the proposed method to significantly increase BCI performance for an MEG-based BCI in an offline analysis as well as an online experiment with 10 subjects. We also show the method to be superior to other covariate shift adaption methods and present examples of identified non-stationaries to show the effect of the proposed method.
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