Abstract: In neurophysiological signal analysis, Ocular Artifacts (OA) are raised differently in Electroencephalographic (EEG). Rejection process of these artifacts is an important research area and finding a method for successful removal of OA incompletely is still a challenge. In this study, Stone blind source separation method (Stone’s BSS) is used to correct the EEG signal by separating OA signals completely, this is a new application for Stone’s BSS in brain signal analysis and there is no one use this method in this field, such that almost previous works based on Independent Component Analysis (ICA), which has some inherent disadvantages. In addition, the modified Stone’s BSS is presented here to interpret how Stone’s BSS deploys generalized Eigenvalue decomposition to obtain the un-mixing matrix based on the responses of two different linear scalar filters to the same set of signals. Therefore, this study opened a new direction field for Stone’s BSS applications. Stone’s BSS method depends on signal temporal predictability measurement for separation processes. A comparison with two well-known BSS algorithms (JADE, FICA) in order to check the Stone’s BSS effectiveness. It is an efficient method to correct the EEG data and can apply it in medical applications as expected. The main purpose of this study is to ascertain the effect of using Stone’s BSS as compared to an Independent Component Analysis (ICA) in isolating the ocular artifacts and correct the EEG data. This method is identified as being of importance in this application and it’s a new direction in the brain signal analysis.