knowing mixed signals and nothing about original signal or how they were mixed. The separated signals are only approximations of the source signals. The separated images, were separated using Python and the Shogun toolbox using Joint Approximation Diagonalization of Eigen-matrices (JADE) algorithm which is based on independent component analysis, ICA. This toolbox method can be used with multi-dimensions but for an easy visual aspect images(2-D) were used. Medical imaging One of the practical applications being researched in this area is medical imaging of the brain with magnetoencephalography (MEG). This kind of imaging involves careful measurements of magnetic fields outside the head which yield an accurate 3D-picture of the interior of the head. However, external sources of electromagnetic fields, such as a wristwatch on the subject's arm, will significantly degrade the accuracy of the measurement. Applying source separation techniques on the measured signals can help remove undesired artifacts from the signal. EEG In electroencephalogram (EEG) and magnetoencephalography (MEG), the interference from muscle activity masks the desired signal from brain activity. BSS, however, can be used to separate the two so an accurate representation of brain activity may be achieved. Music Another application is the separation of musical signals. For a stereo mix of relatively simple signals it is now possible to make a fairly accurate separation, although some artifacts remain. Others Other applications: Communications Stock Prediction Seismic Monitoring Text Document Analysis Mathematical representation The set of individual source signals, , is 'mixed' using a matrix, , to produce a set of 'mixed' signals, , as follows. Usually, is equal to . If , then the system of equations is overdetermined and thus can be unmixed using a conventional linear method. If , the system is underdetermined and a non-linear method must be employed to recover the unmixed signals. The signals themselves can be multidimensional. The above equation is effectively 'inverted' as follows. Blind source separation separates the set of mixed signals, , through the determination of an 'unmixing' matrix, , to 'recover' an approximation of the original signals, . Approaches Since the chief difficulty of the problem is its underdetermination, methods for blind source separation generally seek to narrow the set of possible solutions in a way that is unlikely to exclude the desired solution. In one approach, exemplified by principal and independent component analysis, one seeks source signals that are minimally correlated or maximally independent in a probabilistic or information-theoretic