Joint Cumulant and Correlation Based Signal Separation with Application to Eeg Data Analysis
نویسندگان
چکیده
Current methods in Blind Source Separation (BSS) utilize either the higher order statistics or the time delayed crosscorrelations to perform signal separation. In this paper we investigate a method for source separation which utilizes joint information from higher order statistics and delayed cross-correlations. The algorithm is motivated by problems in analysis of Electroencephalography (EEG) data. We use an EEG analysis example to demonstrate that the Joint Cumulant and Correlation based (JCC) algorithm obtains better source separation than either of the group methods based on higher order statistics or time delayed cross correlations.
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تاریخ انتشار 2001