![]() ![]() To quantitatively examine the performance of the new method, two objective measures were adopted. The proposed method is evaluated in both simulated and real EEG data. Diagonalization methods for IVA in the proposed system were reworked based on SCHUR decomposition offering a faster second order blind identification algorithm that can be used on time demanding applications. The latter was formulated as a general joint Blind Source Separation (BSS) method that uses both second-order and higher order statistical information and thus takes advantage of both Independent Component Analysis (ICA) and Canonical Correlation Analysis (CCA). In the proposed system the muscle artifacts of the EEG signal are removed by using the Independent Vector Analysis (IVA). In this paper, we proposed a new approach in removing muscle artifacts from EEG data using a method that combines second and high order statistical information. These muscle artifacts are particularly difficult to be removed from the EEG in order the latter to be used for further clinical evaluation. EEG recordings are frequently contaminated by muscle artifacts, which obscure and complicate their interpretation. Often, people with Subjective Cognitive Impairment (SCI), Mild Cognitive Impairment (MCI) and dementia are underwent to Electroencephalography (EEG) in order to evaluate through biological indexes the functional connectivity between brain regions and activation areas during cognitive performance. ![]()
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