High Order Differential Covariance based Source Separation of Monkey’s fMRI Data

Blind source separation is a statistical technique in which an observed mixed data is decomposed in source signals and mixing channel. In BSS normally no, or little knowledge about the mixing channel is available a priori. In this work a higher order differential Covariance based source separation technique is used to separate the physiological sources blindly from Monkey’s fMRI data. Proposed covariance based technique is applied first applied to simulated data and then on Monkey’s data. Results are compared with other conventional BSS algorithms. Proposed algorithm outperforms conventional BSS algorithms in terms of quality and computational complexity.