  Objective: 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. EEG recordings are frequently contaminated by muscle artifacts,
which obscure and complicate their interpretation. These muscle artifacts are
particularly difficult to be removed from the EEG in order the latter to be
used for further clinical evaluation. 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. Subjects and Methods: In the proposed
system the muscle artifacts of the EEG signal are removed by using the
Independent Vector Analysis (IVA). 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).
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. Results: The
proposed method is evaluated in both simulated and real EEG data. To
quantitatively examine the performance of the new method, two objective
measures were adopted. The first measure is the Root Mean Square Error (RMSE)
while the second is the Signal-to-noise-ratio (SNR). Conclusion: The proposed
method overcomes with the need of removing muscle artifacts on both realistic
simulated EEG data and brain activity from people with cognitive impairment.
