Adaptive rhythmic component extractionwith regularization for EEG data analysis

Rhythmic component extraction (RCE) is a method for extracting a signal oscillating at a certain frequency from multi-channel sensor signals. This method can be effectively used for detecting rhythmic signals such as alpha and beta waves, which are the feature signals in brain computer/machine interfaces (BCI/BMI). We are addressing a problem in developing an on-line adaptive algorithm for RCE. Since a rhythmic signal in the brain slowly varies, the signals extracted in adjacent frames should not largely different. We propose introducing a regularization term that evaluates the correlation between the signal extracted in the last step and the one to be extracted to achieve this. We show that the maximization of the cost function with the proposed regularization term is reduced to a generalized eigenvalue problem and experimental results from practical EEG data support this analysis.