Elie Bou Assi, Sandy Rihana
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Neural Sensory Systems and Rehabilitation, Brain ComputerInterface, Electroencephalography, Independent ComponentAnalysis, Kmeans clustering.
Electroencephalogram (EEG) recordings are contaminated
by different internal and external noises and interferences.
Therefore, they should be manipulated in order to restore
them from these artifacts that could be eye blinks,
electrocardiogram (ECG) and many others. Recent research
is mainly oriented toward implementing methods in order to
remove ocular artifacts whose frequency band overlap with
the EEG frequency of interest. Independent Component
Analysis (ICA) has already shown to be an effective way for
removing the activity of these artifacts. However, when
implementing an ICA-based method, the key relies on how
to identify the ocular artifact components. Based on the
components characteristics, different features such as
correlation coefficients, distribution ratio, and maximum
value have been identified in order to recognize in an
automatic way the artifactual components and their
subtraction from the original space to get ocular artifacts free
EEG signals. Artifactual components were identified using
an adaptive thresholding by means of K-means clustering.
Qualitative and quantitative techniques of evaluation are
presented and give promising results. The classification
accuracy based on the correlation feature reached 99.54%.